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Recording and online analysis of auditory steady state responses (ASSR) in Matlab
Journal of Neuroscience Methods 187 (2010) 105–113
Contents lists available at ScienceDirect
Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth
Recording and online analysis of auditory steady state responses (ASSR) in Matlab Andreas Bahmer ∗ , Uwe Baumann University of Frankfurt Main, Clinic for Otolaryngology, Audiological Acoustics, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
a r t i c l e
i n f o
Article history: Received 16 November 2009 Received in revised form 15 December 2009 Accepted 16 December 2009 Keywords: ASSR Online analysis Matlab Biosignal EEG
a b s t r a c t Auditory steady state responses (ASSR) are a current research focus because of their potential use as a diagnostic tool. Research platforms are required to test user defined stimuli and algorithms for the analysis of electrophysiologic responses. Commercially available ASSR devices are not adequately flexible. To enable a larger group of scientists to pursue ASSR research, we introduce a cost-efficient and flexible ASSR setup. ASSR recording and online analysis software in Matlab (The Mathworks, Inc.) was developed for a standard PC equipped with an external professional sound card, audiometric headphones, and an EEG biosignal preamplifier. © 2009 Elsevier B.V. All rights reserved.
1. Introduction 1.1. Auditory steady state response Auditory brainstem response (ABR) recordings use transient stimuli to trigger neural responses. The amplitude and latency of neural response peaks are evaluated for diagnostic purposes. In contrast, auditory steady state responses (ASSR) allow automated analysis by statistical evaluation of neural responses. To generate ASSRs, continuous stimuli are presented leading to summate periodic neuronal responses. Amplitude-modulated (AM) pure tones may be used as stimuli. Neural generators, related to the depth and frequency of the AM, are triggered. AM frequencies of 20 Hz or less primarily trigger generators responsible for late cortical evoked potentials (primary auditory cortex and association areas). Response characteristics to 20–50 Hz modulation frequencies are similar to those found in the auditory midbrain, thalamus, and primary auditory cortex. Modulation rates greater than 50 Hz are dominated by evoked potentials from the midbrain, including Jewett wave V. A continuous pure tone with amplitude modulated between 2 and 400 Hz evokes a steady-state response at the modulation frequency (Lins et al., 1996; Picton et al., 1987; Chambers et al., 1968; Kuwada et al., 1986; Rees et al., 1986). The best modulation rates to assess hearing thresholds range between 75 and
∗ Corresponding author. E-mail addresses: [email protected] (A. Bahmer), [email protected] (U. Baumann). 0165-0270/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jneumeth.2009.12.012
110 Hz. At those modulation rates, the ASSR is not significantly influenced by sleep (Cohen et al., 1991; Lins et al., 1995), and, in normal adults, responses to a 1-kHz stimulus can be recorded down to a stimulus level of 26 dB SPL (Lins et al., 1996). Evoked responses derived with continuously presented AM stimuli are analyzed in terms of both spectral energy and phase, and show, with sufficient response activity, a small amplitude response at the frequency of modulation (Chambers et al., 1968; Kuwada et al., 1986; Picton et al., 1987). A time-saving alternative to a single stimulus application, is the simultaneous measurement of responses to several AM stimuli with each stimulus modulated by a different frequency (Regan and Cartwright, 1970; John and Picton, 2000; John et al., 1998; Lins and Picton, 1995; van Dun et al., 2008). Statistical procedures determine whether a response is significant by comparing the response level and/or the phase at the particular modulation frequency to the noise level at adjacent frequencies. The reliability of repeated measurements can also be assessed. In contrast transient stimuli such as clicks or tone pips used to assess frequency-specific responses, steady state stimuli do not need masking noise to reduce the spread of energy into neighboring frequencies. ASSRs allow functional loss in different frequency regions of the cochlea to be assessed. By using frequencyspecific stimuli, different frequency regions are activated and can be evaluated. Cost-efficient research platforms have been introduced using LabView or C/C++ (John and Picton, 2000; van Dun et al., 2008). Our software is implemented in Matlab 7.6.0.324 R2008a (The MathWorks Inc., Natick, MA, USA), is easy to program, and interfaces other hardware components. Easy manipulation of source code,
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without time-consuming compiling, is important when evaluating new stimuli or test paradigms. To test our setup, we evaluated the following: • Whether or not the phase of the modulated input signal is preserved in our hardware and software. • Whether or not the statistical tests (Tcirc and F-test) are sensitive to modulated signals. • Whether or not the setup is capable for performing ASSR. 2. Materials and methods 2.1. Hardware The hardware is described in further details elsewhere (Bahmer et al., 2008). The ASSR setup consists of a standard personal computer, with a 2 GHz Dual Core Intel CPU and 1 GByte RAM, an external sound card (RME Fireface 400, Audio AG, Haimhausen, Germany) for headphones and data acquisition, headphones (HDA 200, Sennheiser electronic GmbH & Co. KG, Wedemark, Germany) and a biosignal preamplifier. The setup is depicted in Fig. 1.
2.1.1. Stimulus presentation The ASSR setup was originally developed to compare statistical test algorithms and stimulus efficiency. However, the hardware itself had not been tested for clinical diagnostics, such as the assessment of hearing thresholds. van Dun et al. (2008) questioned whether the external RME sound card is suitable for clinical diagnostics and stated that the dynamic range of 90 dB may be too narrow for clinical diagnostics. However, by using different sound cards with wider dynamic ranges, this limitation can be overcome. Wider dynamic range sound cards can be integrated into our system because Matlab provides several hardware interfaces. The stimulus used in our ASSR setup is a sinusoidally amplitude modulated (SAM) tone with a modulation depth of 100%. The duration of the tone, the carrier frequency, and the modulation frequency can be selected in our software. We have tested tone durations up to 150 s; longer durations may be limited by the Matlab memory, but it is possible to play files with long durations inside or outside of Matlab. The modulation and carrier frequencies that are entered by the user are adjusted to the buffer length of 1.024 s by the software according to the formula (John and Picton, 2000): f =
int(sf ) s
(1)
where s is the buffer duration in seconds, f is the actual frequency,f is the frequency input by user and int is the function returning closest integer. This adjustment ensures that the buffer length is an integer multiple of the carrier and modulation period, which is required for complete acquisition of the corresponding response period. The tone is generated with the Matlab ‘sound’ function (Matlab 7.6.0.324 R2008a, The MathWorks Inc.) and sent by the software to the RME sound card. The vector data for the ‘sound’ function are calculated in Matlab before it is processed by the ‘sound’ function. We have analyzed the output signal for possible phase shifts by looping back the RME output to its input channel and found that the recorded stimulus contained no phase shifts. The test level (90 dB SPL) was calibrated with an artificial ear (Type 4153, Brüel and Kjær, Nærum, Denmark). Other sound pressure levels can be selected by adjusting the external RME sound card’s attenuator. 2.1.2. Data acquisition The preamplifier is a single-channel, analog medical grade isolation amplifier IA 297 (Intronics Power, Inc., Norwood, MA, USA), which allows recording of EEG and electrode impedance measurements. It provides full patient protection from leakage currents and amplifier fault currents. Technical data are as follows: common mode rejection of the IA 297 is 170 dB with a balanced source impedance (160 dB with a 5 k source imbalance), noise voltage is 0.3 V RMS 10 Hz to 1 kHz, current noise is 4 pA RMS 0.05 Hz to 1 kHz, input bias current 200 pA, bandwidth 10 kHz, and the gain of the IA 297 is 10 V/V (non-inverting). The overall gain of the entire preamplifier is 10 V/mV. Power supply is provided by USB or battery. The EEG data from the preamplifier is acquired with a RME Fireface 8-channel external low latency audio interface (RME Fireface 400, Audio AG, Haimhausen, Germany). We chose this sound card because it is inexpensive and has a specified dynamic range of 24 bits. The sound card is equipped with 8 input and 8 output channels that can be used simultaneously. EEG data is recorded at sound card channel 1 and the data are stored in the Matlab workspace and can be saved for offline evaluation. 2.2. Software
Fig. 1. Hardware setup for ASSR.
We developed a graphical user interface in Matlab 7.6.0.324 R2008a which allows operation of the following components and
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visualization of the results. The software is based on functions described elsewhere (Bahmer et al., 2008). 2.2.1. Fourier transformation The Fourier Transformation of the signal is accomplished by a built-in function in Matlab. Matlab performs a discrete Fourier transformation computed with a fast Fourier (FFT) algorithm. The frequency resolution of the FFT, with data sampling 44.1 kHz and an epoch length of 1.024 s, is 0.9766 Hz. Four our test purposes, the resolution was sufficient. For setups using the MASTER technique (John and Picton, 2000; van Dun et al., 2008), the resolution can be enhanced by linking several epochs together resulting in longer data windows that can be transformed. For example, John and Picton (2000) stated that 16 epochs were transformed producing a frequency resolution of 0.061 Hz at a sampling rate of 1000 Hz. An FFT of the average of the sampled epochs can be performed to increase the signal-to-noise ratio. 2.2.2. Statistical analyses Two statistical tests for evaluating response significance are implemented in our software. Both allow signal detection for a given significance threshold. One test analyzes and compares Fourier components of several subsequent measurements at the frequency of the signal modulation (Victor and Mast, 1991). The other test evaluates Fourier components that are adjacent to the modulation frequency; these adjacent components are used to estimate the noise level (e.g., John and Picton, 2000). In addition, averaged data can be analyzed. Both test methods apply the F-test to estimate the probability that two subsets originate from the same probability distribution. In our software, the response is considered significantly different from noise when the probability is less than 0.05. This significance level means that in 100 epoch measurements, less than 5 are considered false positives. The response is taken as a measure of hearing the modulated tone. We implemented these two tests because they represent two different algorithms. They differ from each other in their analysis, whereas other tests are essentially variations of these two basic tests. For example in the inclusion of phase or/and amplitude, or in the number of noise estimators, or in the number of repetitions (cf., Stuerzebecher et al., 1999; Cebulla et al., 2001, 2006). 2.2.3. Tcirc test The first method tests the null hypothesis that there is no signal present by comparing the distribution of repeated measurements to a theoretical distribution. The derivation of this test is as follows (for details see Victor and Mast, 1991): Denote M estimates of Fourier components z1 , z2 , . . . ,zM , with an empirical mean value zest = ( zj /M). Assume an a priori hypothetical mean value by . Quantities zj , zest , and are complex numbers, and, therefore, there are two independent estimates of the population variance V of real and imaginary parts with 2(M − 1) degrees of freedom. One estimate for the population variance V is Vindiv =
1 2(M − 1)
|zj − zest |2
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mean is . Therefore, the ratio Vgroup /Vindiv is distributed according to the F distribution. A definition of Tcirc is 2 Tcirc =
1 Vgroup M Vindiv
(4)
2 is distributed according to the F MTcirc [2,2M−2] distribution. The null hypothesis that no signal is present equals = 0. It can be rejected if the calculated value of F is under a specified confidence level (here p < .05). The entire recorded data is used for the calculation of the F value.
2.2.4. F-test The second method tests the hypothesis that a signal is present by comparing adjacent components to the component at the modulation frequency (Lins et al., 1995; Zurek, 1992; John and Picton, 2000). If the component that is tested does not fall into the same probability distribution as the remaining components, the hypothesis is rejected. In the software implementation, the hypothesis is tested using an F-test that evaluates the ratio between the power at the signal frequency and the average power at adjacent frequency bins (Lins et al., 1996). In the present study, the number of adjacent frequency bins is M = 60, equally distributed around the signal frequency. The significance is tested against critical values for F (here p < .05) for the number of degrees of freedom for the signal (2) and for the adjacent components (2M − 1) (John and Picton, 2000). F=
|FFT(signal)|2 avg(|FFT(noise)|2 )
(5)
where FFT(signal) is the power at frequency to be tested, FFT(noise) is the power at adjacent frequency bins and avg is the average function. 3. Results 3.1. Phase preservation Because the statistical tests are amplitude and phase sensitive, the recording setup was checked for phase preservation. A sinusoidal signal at 97.656 Hz (the epoch duration is an integer multiple of 97.656 Hz) from a signal generator was the input signal for the analog/digital converter (RME, Fireface 400, sampling at 44.1 kHz). In Fig. 2, FFT curves from the analog/digital converter output are shown. The setup produces no phase shifts generating canceling effects, which would result in lower amplitudes for the stem plot compared to the solid curve. Both statistical tests (Tcirc and F-test) showed signal significance at 97.656 Hz. For further analysis, we inserted a phase shift by lowering the frequency of the signal generator to 96 Hz while leaving the frequency that was analyzed at 97.656 Hz. As the epoch duration was set to a multiple of 97.656 Hz, deviations from this frequency result
(2)
The second estimate for the population variance V depends on the assumption that the population mean is . zest is the mean of M independent estimates with each real and imaginary parts variance V/M. Vgroup =
M 2
|zest − |2
(3)
Vgroup and Vindiv are estimates derived from independent quantities under the hypothesis that data zj are samples of a population whose
Fig. 2. Setup tested with a sinusoidal signal at 97.656 Hz. The recorded epoch is an integer multiple of the signal period. The FFT reflects the input signal. The continuous line indicates the FFT of one epoch (1.024 s), the stem plot indicates the FFT of 12 averaged epochs (frequency resolution 0.98 Hz).
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Fig. 3. Setup tested with a sinusoidal signal at 96 Hz (deviates from analyzed frequency). The continuous line indicates the FFT of one epoch, the stem plot indicates the FFT of 12 averaged epochs. The epoch is not an integer multiple of the signal period, therefore the FFT of one epoch does not cover the FFT of the averaged signal.
in canceling effects. This effect is demonstrated in Fig. 3. The FFT of the averaged signal (stem plot) deviates from the FFT of one epoch (solid curve). The disruption of the signal is also reflected in the responses of the statistical tests (Fig. 4). Consequently, they are both nonsignificant. In addition, the Tcirc test shows strong beating waves, whereas the F-test shows only small beating waves. The difference between both beating wave intensities can be explained by the additional evaluation of phase information of the Tcirc test. For reliable results using the Tcirc test (in Fig. 4 after one epoch), several measurements are evaluated because only a single channel is analyzed. In contrast, the F-test compares adjacent channels which contain sufficient information in a single epoch for reliable test responses (Fig. 4). Fig. 5. Significance test for a sinusoidal signal from a signal generator (97.656 Hz) recorded with a subjects’s EEG. The signal is switched on at the location of the vertical line. Shortly after the signal is switched on, the statistic tests show significance. Horizontal line: significance level (p = .05), x-axis: number of recorded epochs, yaxis: test probability.
3.2. Evaluation of the statistical tests The two implemented statistical tests were evaluated by recording a subject’s EEG mixed together with a sinusoidal wave from a signal generator at 97.656 Hz. Signal root-mean-square (RMS) was selected to be equal to noise RMS. In Fig. 5 the horizontal line indicates the time when the signal was switched on. The Tcirc test probability values changed after switching on from p = 0.8 to significance (p < .05) after ∼ 49 epochs (∼ 50 s); the F-test probability values changed from p = 0.8 to significance (p < .05) after ∼ 48 epochs (∼ 49 s). 3.3. ASSR measurements
Fig. 4. Statistical tests of a sinusoidal signal at 96 Hz with analysis at 97.656 Hz (as in Fig. 3). Because the Tcirc is phase sensitive, the results show stronger beating waves than the F-test. Horizontal line: significance level (p = .05), x-axis: number of epochs, y-axis: test probability.
An ASSR was performed with a normal hearing human subject. The plots in the graphical user interface in Fig. 6 show the EEG response, the FFT, and the statistical test responses with a SAM (1 kHz carrier, modulation 97.656 Hz, 90 dB SPL) applied monotically (right ear) by headphones. The statistical test Tcirc showed signal significance after 5 epochs (5 s, Tcirc ); the F-test showed significance after 10 epochs (10 s). The peaks in the lower spectrum were due to the effect of mains. To evaluate the test specificity (significance is reached only if signal is present), an EEG from a normal hearing subject was recorded for 20 s. After this, a SAM signal was switched on (Fig. 7, carrier 1 kHz, modulation 97.656 Hz, 90 dB SPL). About 40 s after switch on, the Tcirc test reached significance (p <.05); the F-test reached significance after 50 s. The tests showed specificity as their responses were clearly related to the signal. The presence of the signal was controlled acoustically. The peaks in the lower spectrum were due to the effect of mains.
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Fig. 6. ASSR with normal hearing subject (monotical headphone presentation: SAM at a 1 kHz carrier, modulation 97.656 Hz, 90 dB SPL). Left side. Top to bottom axis: raw and averaged data, averaged FFT (scaling 86–110 Hz), and averaged FFT (scaling 0–5 kHz). Right side. Top to bottom axis: FFT phase plot, Tcirc probability, and F-test probability. The Tcirc test reaches significance after 5 epochs (5 s), the F-test after 10 epochs (10 s).
Fig. 7. ASSR with normal hearing subject but with the beginning of SAM signal delayed (headphone presentation: sinusoidal amplitude modulated sine wave, carrier 1 kHz, modulation 97.656 Hz, 90 dB SPL, description of plots in Fig. 6). The Tcirc test reaches significance after ∼40 s after switch on (at 20 s); the F-test reaches significance after ∼50 s.
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Electro-magnetic interference caused by the headphone receivers was investigated by application of an SAM signal to the headphone. An electrical circuit connected to the biosignal preamplifier placed in close proximity to the headphone was recorded. The circuit consisted of 3 circularly arranged resistors (each 2 k). Each juncture of these resistors was connected via a 1.5 k resistor with the preamplifier (Lehnhardt, 1996). There was no incidence for an artifact at the tested frequency caused by the headphones’ inductive signal. To further evaluate human subject ASSRs, a set of carrier and modulation frequencies was tested (.5, 1, 2, and 4 kHz carriers at an AM of 97.656 Hz, modulation frequencies 82, 86, 90, 94, 98, 102, 106, and 110 Hz at 500 Hz, 90 dB SPL). All tests showed significance (p < .05). These frequencies were tested because they were used simultaneously in the multiple auditory steady-state evoked response (MASTER) technique (Regan and Cartwright, 1970; John and Picton, 2000; John et al., 1998; Lins and Picton, 1995). Simultaneous stimulation reduces the testing time.
most circumstances, ‘getdata’ returns all requested data and does not miss any samples” (MathWorks, 2007). In our tests, this function did not miss any samples at epoch lengths of 1.024 s and a sampling rate of 44.1 kHz. The selection of the headphone and amplifier also plays an important role for successful measurements of ASSR because phase and amplitude reproduction of the signal modulation is crucial. In our setup, a Sennheiser headphone (HDA 200, Sennheiser electronic GmbH & Co. KG, Wedemark, Germany) was connected to the amplifier of an high quality sound card (RME Fireface 400). The quality of the headphone is important, whereas the quality of the amplifier may be less important, at least at low SPLs. At higher levels, amplifier distortions may increase and disrupt correct phase reproduction. We have not yet tested devices with lower quality such as built-in sound cards or cheaper headphones, but these tests can be conducted with our setup because hardware configurations can be easily modified by the user. The aim of future research will be the calibration of the setup for diagnostic purposes, such as threshold detection and comparison with other ASSR devices.
4. Discussion
4.2. Statical tests
In this paper, we described the setup for a cost-efficient auditory steady state response (ASSR) recording device for Matlab. The hardware and software implementation were outlined and the setup was evaluated on a normal-hearing subject. With this cost-efficient hardware and software, an alternative research setup to commercially available systems was developed. This setup is mobile, robust, inexpensive, flexible and is a modular research platform, capable of processing custom made stimuli. Unique stimuli or altered stimuli and recording parameters may be used in contrast to commercial devices. In commercial setups, changing functionalities without support from the manufacturer is seldom possible. In our software, the program code can be modified in Matlab without extra compiling. The program code is ordered by functions for easy handling. In the initialization function, the sampling rate (default 44.1 kHz), carrier and modulation frequency for acoustical stimulation and hardware parameters for the data acquisition and headphone interface can be selected. The recording function contains several parameters which can be modified, such as the number of recorded epochs that are linked for increasing FFT resolution, averaging for the F-test, and statistical test parameters. For example, for the F-test, the number of neighboring channels or the inclusion of phase information can be selected. Although not described in this paper, tests with a software module that can control cochlear implants via a research interface box (RIB2, MEDEL GmbH, Innsbruck, Austria) were successfully performed. Using this module, electrical auditory steady state responses can be evaluated.
In our software, two substantially different statistical tests (Tcirc and F-test) are implemented. The first one, the Tcirc test, evaluates information from only a single frequency channel. This is advantageous because it assures independence from neighboring channels, which can contain disturbing signals. The disadvantage is low sensitivity for artificial signals (Fig. 4). In this case, for reliable test performance, subsequent measurements compensate for the missing information from neighboring channels. Consequently, the Tcirc test initially shows false positive test results. After one or several measurements, test responses are reliable. If the test signal contained phase alterations then the Tcirc test showed strong beating waves in the probability results because of its phase sensitivity (Fig. 4). In contrast, the F-test values showed only small beating waves. This disadvantage for artificial signals disappeared in tests with EEG signals (Figs. 5–7). Furthermore, the Tcirc test reached significance faster than the F-test. In the F-test, a number of neighboring channels (in this study n = 60) are compared to a single frequency channel. The information from the neighboring channels constitute an estimator for the noise in the EEG. Especially for artificial signals, the supplemental information from neighboring channels contributed to quickly reaching test significance and reliable responses at the beginning of the test (Fig. 4). For ASSR in normal hearing subjects, the Tcirc test may be superior (Figs. 6 and 7).
4.1. Phase coherence Our online recording and calibration tests showed precise and reliable responses of our setup after stimulation with acoustic stimuli. The sound card latency jitter may be a critical parameter for the online analysis because several recorded epochs must exactly match in their phase. We used a low latency external RME sound card (RME Fireface 400, Audio AG, Haimhausen, Germany) at a sampling rate of 44.1 kHz. We have not yet performed the test with built-in sound cards, but if the sampling rate is at least 44.1 kHz and latency jitter is low, reliable responses can be expected. In our software, the function ‘getdata’ from the Matlab Data Acquisition Toolbox (Version 2.12, R2008a, Matlab 7.6.0.324 R2008a, The MathWorks Inc.) is used. The manuals states that “in
4.3. Stability Hardware interfaces (data acquisition and headphone control), online FFT and online analysis, and diagram plotting in the graphical user interface showed fast performance with different test paradigms and were robust against errors. In contrast to tests using commercial devices, which sometimes produced software conflicts, our setup showed reliable performance in up to about 100 test runs.
5. Conclusions • A low-cost robust hardware and software environment in Matlab for ASSR was developed. • The phase of the modulated input signal was preserved in our hardware and software. • The two statistical tests (Tcirc and F-test) were sensitive to modulated signals.
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• The setup can be used for ASSR because the tests showed reliable responses to modulated signals. Acknowledgements We would like to thank Dr. J.D. Victor, Cornell University, NY, for assistance with the Tcirc test, two anonymous reviewers for their helpful comments, and Dr. Jane M. Opie for improvements to our English language usage. The work was supported by the grant from the DFG (BA 2085/3-1).
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Appendix A. A.1. Matlab code in main program for Tcirc and F-test The program code for the statistical test Tcirc (Victor and Mast, 1991) and F-test (e.g., John and Picton, 2000) are printed in the following. The used function “tcirc” is described below, the function “fcdf” is a function from the Matlab Statistics Toolbox (Version 6.2, R2008a). “fcdf” is the F cumulative distribution function. The number “loc” indicates the frequency channel that is tested.
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A.2. Matlab code for Tcirc function In the following the program code for the Tcirc function is printed. For more details see Section 1 and the paper from Victor and Mast (1991).
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Contents lists available at ScienceDirect
Journal of Neuroscience Methods journal homepage: www.elsevier.com/locate/jneumeth
Recording and online analysis of auditory steady state responses (ASSR) in Matlab Andreas Bahmer ∗ , Uwe Baumann University of Frankfurt Main, Clinic for Otolaryngology, Audiological Acoustics, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
a r t i c l e
i n f o
Article history: Received 16 November 2009 Received in revised form 15 December 2009 Accepted 16 December 2009 Keywords: ASSR Online analysis Matlab Biosignal EEG
a b s t r a c t Auditory steady state responses (ASSR) are a current research focus because of their potential use as a diagnostic tool. Research platforms are required to test user defined stimuli and algorithms for the analysis of electrophysiologic responses. Commercially available ASSR devices are not adequately flexible. To enable a larger group of scientists to pursue ASSR research, we introduce a cost-efficient and flexible ASSR setup. ASSR recording and online analysis software in Matlab (The Mathworks, Inc.) was developed for a standard PC equipped with an external professional sound card, audiometric headphones, and an EEG biosignal preamplifier. © 2009 Elsevier B.V. All rights reserved.
1. Introduction 1.1. Auditory steady state response Auditory brainstem response (ABR) recordings use transient stimuli to trigger neural responses. The amplitude and latency of neural response peaks are evaluated for diagnostic purposes. In contrast, auditory steady state responses (ASSR) allow automated analysis by statistical evaluation of neural responses. To generate ASSRs, continuous stimuli are presented leading to summate periodic neuronal responses. Amplitude-modulated (AM) pure tones may be used as stimuli. Neural generators, related to the depth and frequency of the AM, are triggered. AM frequencies of 20 Hz or less primarily trigger generators responsible for late cortical evoked potentials (primary auditory cortex and association areas). Response characteristics to 20–50 Hz modulation frequencies are similar to those found in the auditory midbrain, thalamus, and primary auditory cortex. Modulation rates greater than 50 Hz are dominated by evoked potentials from the midbrain, including Jewett wave V. A continuous pure tone with amplitude modulated between 2 and 400 Hz evokes a steady-state response at the modulation frequency (Lins et al., 1996; Picton et al., 1987; Chambers et al., 1968; Kuwada et al., 1986; Rees et al., 1986). The best modulation rates to assess hearing thresholds range between 75 and
∗ Corresponding author. E-mail addresses: [email protected] (A. Bahmer), [email protected] (U. Baumann). 0165-0270/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.jneumeth.2009.12.012
110 Hz. At those modulation rates, the ASSR is not significantly influenced by sleep (Cohen et al., 1991; Lins et al., 1995), and, in normal adults, responses to a 1-kHz stimulus can be recorded down to a stimulus level of 26 dB SPL (Lins et al., 1996). Evoked responses derived with continuously presented AM stimuli are analyzed in terms of both spectral energy and phase, and show, with sufficient response activity, a small amplitude response at the frequency of modulation (Chambers et al., 1968; Kuwada et al., 1986; Picton et al., 1987). A time-saving alternative to a single stimulus application, is the simultaneous measurement of responses to several AM stimuli with each stimulus modulated by a different frequency (Regan and Cartwright, 1970; John and Picton, 2000; John et al., 1998; Lins and Picton, 1995; van Dun et al., 2008). Statistical procedures determine whether a response is significant by comparing the response level and/or the phase at the particular modulation frequency to the noise level at adjacent frequencies. The reliability of repeated measurements can also be assessed. In contrast transient stimuli such as clicks or tone pips used to assess frequency-specific responses, steady state stimuli do not need masking noise to reduce the spread of energy into neighboring frequencies. ASSRs allow functional loss in different frequency regions of the cochlea to be assessed. By using frequencyspecific stimuli, different frequency regions are activated and can be evaluated. Cost-efficient research platforms have been introduced using LabView or C/C++ (John and Picton, 2000; van Dun et al., 2008). Our software is implemented in Matlab 7.6.0.324 R2008a (The MathWorks Inc., Natick, MA, USA), is easy to program, and interfaces other hardware components. Easy manipulation of source code,
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without time-consuming compiling, is important when evaluating new stimuli or test paradigms. To test our setup, we evaluated the following: • Whether or not the phase of the modulated input signal is preserved in our hardware and software. • Whether or not the statistical tests (Tcirc and F-test) are sensitive to modulated signals. • Whether or not the setup is capable for performing ASSR. 2. Materials and methods 2.1. Hardware The hardware is described in further details elsewhere (Bahmer et al., 2008). The ASSR setup consists of a standard personal computer, with a 2 GHz Dual Core Intel CPU and 1 GByte RAM, an external sound card (RME Fireface 400, Audio AG, Haimhausen, Germany) for headphones and data acquisition, headphones (HDA 200, Sennheiser electronic GmbH & Co. KG, Wedemark, Germany) and a biosignal preamplifier. The setup is depicted in Fig. 1.
2.1.1. Stimulus presentation The ASSR setup was originally developed to compare statistical test algorithms and stimulus efficiency. However, the hardware itself had not been tested for clinical diagnostics, such as the assessment of hearing thresholds. van Dun et al. (2008) questioned whether the external RME sound card is suitable for clinical diagnostics and stated that the dynamic range of 90 dB may be too narrow for clinical diagnostics. However, by using different sound cards with wider dynamic ranges, this limitation can be overcome. Wider dynamic range sound cards can be integrated into our system because Matlab provides several hardware interfaces. The stimulus used in our ASSR setup is a sinusoidally amplitude modulated (SAM) tone with a modulation depth of 100%. The duration of the tone, the carrier frequency, and the modulation frequency can be selected in our software. We have tested tone durations up to 150 s; longer durations may be limited by the Matlab memory, but it is possible to play files with long durations inside or outside of Matlab. The modulation and carrier frequencies that are entered by the user are adjusted to the buffer length of 1.024 s by the software according to the formula (John and Picton, 2000): f =
int(sf ) s
(1)
where s is the buffer duration in seconds, f is the actual frequency,f is the frequency input by user and int is the function returning closest integer. This adjustment ensures that the buffer length is an integer multiple of the carrier and modulation period, which is required for complete acquisition of the corresponding response period. The tone is generated with the Matlab ‘sound’ function (Matlab 7.6.0.324 R2008a, The MathWorks Inc.) and sent by the software to the RME sound card. The vector data for the ‘sound’ function are calculated in Matlab before it is processed by the ‘sound’ function. We have analyzed the output signal for possible phase shifts by looping back the RME output to its input channel and found that the recorded stimulus contained no phase shifts. The test level (90 dB SPL) was calibrated with an artificial ear (Type 4153, Brüel and Kjær, Nærum, Denmark). Other sound pressure levels can be selected by adjusting the external RME sound card’s attenuator. 2.1.2. Data acquisition The preamplifier is a single-channel, analog medical grade isolation amplifier IA 297 (Intronics Power, Inc., Norwood, MA, USA), which allows recording of EEG and electrode impedance measurements. It provides full patient protection from leakage currents and amplifier fault currents. Technical data are as follows: common mode rejection of the IA 297 is 170 dB with a balanced source impedance (160 dB with a 5 k source imbalance), noise voltage is 0.3 V RMS 10 Hz to 1 kHz, current noise is 4 pA RMS 0.05 Hz to 1 kHz, input bias current 200 pA, bandwidth 10 kHz, and the gain of the IA 297 is 10 V/V (non-inverting). The overall gain of the entire preamplifier is 10 V/mV. Power supply is provided by USB or battery. The EEG data from the preamplifier is acquired with a RME Fireface 8-channel external low latency audio interface (RME Fireface 400, Audio AG, Haimhausen, Germany). We chose this sound card because it is inexpensive and has a specified dynamic range of 24 bits. The sound card is equipped with 8 input and 8 output channels that can be used simultaneously. EEG data is recorded at sound card channel 1 and the data are stored in the Matlab workspace and can be saved for offline evaluation. 2.2. Software
Fig. 1. Hardware setup for ASSR.
We developed a graphical user interface in Matlab 7.6.0.324 R2008a which allows operation of the following components and
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visualization of the results. The software is based on functions described elsewhere (Bahmer et al., 2008). 2.2.1. Fourier transformation The Fourier Transformation of the signal is accomplished by a built-in function in Matlab. Matlab performs a discrete Fourier transformation computed with a fast Fourier (FFT) algorithm. The frequency resolution of the FFT, with data sampling 44.1 kHz and an epoch length of 1.024 s, is 0.9766 Hz. Four our test purposes, the resolution was sufficient. For setups using the MASTER technique (John and Picton, 2000; van Dun et al., 2008), the resolution can be enhanced by linking several epochs together resulting in longer data windows that can be transformed. For example, John and Picton (2000) stated that 16 epochs were transformed producing a frequency resolution of 0.061 Hz at a sampling rate of 1000 Hz. An FFT of the average of the sampled epochs can be performed to increase the signal-to-noise ratio. 2.2.2. Statistical analyses Two statistical tests for evaluating response significance are implemented in our software. Both allow signal detection for a given significance threshold. One test analyzes and compares Fourier components of several subsequent measurements at the frequency of the signal modulation (Victor and Mast, 1991). The other test evaluates Fourier components that are adjacent to the modulation frequency; these adjacent components are used to estimate the noise level (e.g., John and Picton, 2000). In addition, averaged data can be analyzed. Both test methods apply the F-test to estimate the probability that two subsets originate from the same probability distribution. In our software, the response is considered significantly different from noise when the probability is less than 0.05. This significance level means that in 100 epoch measurements, less than 5 are considered false positives. The response is taken as a measure of hearing the modulated tone. We implemented these two tests because they represent two different algorithms. They differ from each other in their analysis, whereas other tests are essentially variations of these two basic tests. For example in the inclusion of phase or/and amplitude, or in the number of noise estimators, or in the number of repetitions (cf., Stuerzebecher et al., 1999; Cebulla et al., 2001, 2006). 2.2.3. Tcirc test The first method tests the null hypothesis that there is no signal present by comparing the distribution of repeated measurements to a theoretical distribution. The derivation of this test is as follows (for details see Victor and Mast, 1991): Denote M estimates of Fourier components z1 , z2 , . . . ,zM , with an empirical mean value zest = ( zj /M). Assume an a priori hypothetical mean value by . Quantities zj , zest , and are complex numbers, and, therefore, there are two independent estimates of the population variance V of real and imaginary parts with 2(M − 1) degrees of freedom. One estimate for the population variance V is Vindiv =
1 2(M − 1)
|zj − zest |2
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mean is . Therefore, the ratio Vgroup /Vindiv is distributed according to the F distribution. A definition of Tcirc is 2 Tcirc =
1 Vgroup M Vindiv
(4)
2 is distributed according to the F MTcirc [2,2M−2] distribution. The null hypothesis that no signal is present equals = 0. It can be rejected if the calculated value of F is under a specified confidence level (here p < .05). The entire recorded data is used for the calculation of the F value.
2.2.4. F-test The second method tests the hypothesis that a signal is present by comparing adjacent components to the component at the modulation frequency (Lins et al., 1995; Zurek, 1992; John and Picton, 2000). If the component that is tested does not fall into the same probability distribution as the remaining components, the hypothesis is rejected. In the software implementation, the hypothesis is tested using an F-test that evaluates the ratio between the power at the signal frequency and the average power at adjacent frequency bins (Lins et al., 1996). In the present study, the number of adjacent frequency bins is M = 60, equally distributed around the signal frequency. The significance is tested against critical values for F (here p < .05) for the number of degrees of freedom for the signal (2) and for the adjacent components (2M − 1) (John and Picton, 2000). F=
|FFT(signal)|2 avg(|FFT(noise)|2 )
(5)
where FFT(signal) is the power at frequency to be tested, FFT(noise) is the power at adjacent frequency bins and avg is the average function. 3. Results 3.1. Phase preservation Because the statistical tests are amplitude and phase sensitive, the recording setup was checked for phase preservation. A sinusoidal signal at 97.656 Hz (the epoch duration is an integer multiple of 97.656 Hz) from a signal generator was the input signal for the analog/digital converter (RME, Fireface 400, sampling at 44.1 kHz). In Fig. 2, FFT curves from the analog/digital converter output are shown. The setup produces no phase shifts generating canceling effects, which would result in lower amplitudes for the stem plot compared to the solid curve. Both statistical tests (Tcirc and F-test) showed signal significance at 97.656 Hz. For further analysis, we inserted a phase shift by lowering the frequency of the signal generator to 96 Hz while leaving the frequency that was analyzed at 97.656 Hz. As the epoch duration was set to a multiple of 97.656 Hz, deviations from this frequency result
(2)
The second estimate for the population variance V depends on the assumption that the population mean is . zest is the mean of M independent estimates with each real and imaginary parts variance V/M. Vgroup =
M 2
|zest − |2
(3)
Vgroup and Vindiv are estimates derived from independent quantities under the hypothesis that data zj are samples of a population whose
Fig. 2. Setup tested with a sinusoidal signal at 97.656 Hz. The recorded epoch is an integer multiple of the signal period. The FFT reflects the input signal. The continuous line indicates the FFT of one epoch (1.024 s), the stem plot indicates the FFT of 12 averaged epochs (frequency resolution 0.98 Hz).
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Fig. 3. Setup tested with a sinusoidal signal at 96 Hz (deviates from analyzed frequency). The continuous line indicates the FFT of one epoch, the stem plot indicates the FFT of 12 averaged epochs. The epoch is not an integer multiple of the signal period, therefore the FFT of one epoch does not cover the FFT of the averaged signal.
in canceling effects. This effect is demonstrated in Fig. 3. The FFT of the averaged signal (stem plot) deviates from the FFT of one epoch (solid curve). The disruption of the signal is also reflected in the responses of the statistical tests (Fig. 4). Consequently, they are both nonsignificant. In addition, the Tcirc test shows strong beating waves, whereas the F-test shows only small beating waves. The difference between both beating wave intensities can be explained by the additional evaluation of phase information of the Tcirc test. For reliable results using the Tcirc test (in Fig. 4 after one epoch), several measurements are evaluated because only a single channel is analyzed. In contrast, the F-test compares adjacent channels which contain sufficient information in a single epoch for reliable test responses (Fig. 4). Fig. 5. Significance test for a sinusoidal signal from a signal generator (97.656 Hz) recorded with a subjects’s EEG. The signal is switched on at the location of the vertical line. Shortly after the signal is switched on, the statistic tests show significance. Horizontal line: significance level (p = .05), x-axis: number of recorded epochs, yaxis: test probability.
3.2. Evaluation of the statistical tests The two implemented statistical tests were evaluated by recording a subject’s EEG mixed together with a sinusoidal wave from a signal generator at 97.656 Hz. Signal root-mean-square (RMS) was selected to be equal to noise RMS. In Fig. 5 the horizontal line indicates the time when the signal was switched on. The Tcirc test probability values changed after switching on from p = 0.8 to significance (p < .05) after ∼ 49 epochs (∼ 50 s); the F-test probability values changed from p = 0.8 to significance (p < .05) after ∼ 48 epochs (∼ 49 s). 3.3. ASSR measurements
Fig. 4. Statistical tests of a sinusoidal signal at 96 Hz with analysis at 97.656 Hz (as in Fig. 3). Because the Tcirc is phase sensitive, the results show stronger beating waves than the F-test. Horizontal line: significance level (p = .05), x-axis: number of epochs, y-axis: test probability.
An ASSR was performed with a normal hearing human subject. The plots in the graphical user interface in Fig. 6 show the EEG response, the FFT, and the statistical test responses with a SAM (1 kHz carrier, modulation 97.656 Hz, 90 dB SPL) applied monotically (right ear) by headphones. The statistical test Tcirc showed signal significance after 5 epochs (5 s, Tcirc ); the F-test showed significance after 10 epochs (10 s). The peaks in the lower spectrum were due to the effect of mains. To evaluate the test specificity (significance is reached only if signal is present), an EEG from a normal hearing subject was recorded for 20 s. After this, a SAM signal was switched on (Fig. 7, carrier 1 kHz, modulation 97.656 Hz, 90 dB SPL). About 40 s after switch on, the Tcirc test reached significance (p <.05); the F-test reached significance after 50 s. The tests showed specificity as their responses were clearly related to the signal. The presence of the signal was controlled acoustically. The peaks in the lower spectrum were due to the effect of mains.
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Fig. 6. ASSR with normal hearing subject (monotical headphone presentation: SAM at a 1 kHz carrier, modulation 97.656 Hz, 90 dB SPL). Left side. Top to bottom axis: raw and averaged data, averaged FFT (scaling 86–110 Hz), and averaged FFT (scaling 0–5 kHz). Right side. Top to bottom axis: FFT phase plot, Tcirc probability, and F-test probability. The Tcirc test reaches significance after 5 epochs (5 s), the F-test after 10 epochs (10 s).
Fig. 7. ASSR with normal hearing subject but with the beginning of SAM signal delayed (headphone presentation: sinusoidal amplitude modulated sine wave, carrier 1 kHz, modulation 97.656 Hz, 90 dB SPL, description of plots in Fig. 6). The Tcirc test reaches significance after ∼40 s after switch on (at 20 s); the F-test reaches significance after ∼50 s.
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Electro-magnetic interference caused by the headphone receivers was investigated by application of an SAM signal to the headphone. An electrical circuit connected to the biosignal preamplifier placed in close proximity to the headphone was recorded. The circuit consisted of 3 circularly arranged resistors (each 2 k). Each juncture of these resistors was connected via a 1.5 k resistor with the preamplifier (Lehnhardt, 1996). There was no incidence for an artifact at the tested frequency caused by the headphones’ inductive signal. To further evaluate human subject ASSRs, a set of carrier and modulation frequencies was tested (.5, 1, 2, and 4 kHz carriers at an AM of 97.656 Hz, modulation frequencies 82, 86, 90, 94, 98, 102, 106, and 110 Hz at 500 Hz, 90 dB SPL). All tests showed significance (p < .05). These frequencies were tested because they were used simultaneously in the multiple auditory steady-state evoked response (MASTER) technique (Regan and Cartwright, 1970; John and Picton, 2000; John et al., 1998; Lins and Picton, 1995). Simultaneous stimulation reduces the testing time.
most circumstances, ‘getdata’ returns all requested data and does not miss any samples” (MathWorks, 2007). In our tests, this function did not miss any samples at epoch lengths of 1.024 s and a sampling rate of 44.1 kHz. The selection of the headphone and amplifier also plays an important role for successful measurements of ASSR because phase and amplitude reproduction of the signal modulation is crucial. In our setup, a Sennheiser headphone (HDA 200, Sennheiser electronic GmbH & Co. KG, Wedemark, Germany) was connected to the amplifier of an high quality sound card (RME Fireface 400). The quality of the headphone is important, whereas the quality of the amplifier may be less important, at least at low SPLs. At higher levels, amplifier distortions may increase and disrupt correct phase reproduction. We have not yet tested devices with lower quality such as built-in sound cards or cheaper headphones, but these tests can be conducted with our setup because hardware configurations can be easily modified by the user. The aim of future research will be the calibration of the setup for diagnostic purposes, such as threshold detection and comparison with other ASSR devices.
4. Discussion
4.2. Statical tests
In this paper, we described the setup for a cost-efficient auditory steady state response (ASSR) recording device for Matlab. The hardware and software implementation were outlined and the setup was evaluated on a normal-hearing subject. With this cost-efficient hardware and software, an alternative research setup to commercially available systems was developed. This setup is mobile, robust, inexpensive, flexible and is a modular research platform, capable of processing custom made stimuli. Unique stimuli or altered stimuli and recording parameters may be used in contrast to commercial devices. In commercial setups, changing functionalities without support from the manufacturer is seldom possible. In our software, the program code can be modified in Matlab without extra compiling. The program code is ordered by functions for easy handling. In the initialization function, the sampling rate (default 44.1 kHz), carrier and modulation frequency for acoustical stimulation and hardware parameters for the data acquisition and headphone interface can be selected. The recording function contains several parameters which can be modified, such as the number of recorded epochs that are linked for increasing FFT resolution, averaging for the F-test, and statistical test parameters. For example, for the F-test, the number of neighboring channels or the inclusion of phase information can be selected. Although not described in this paper, tests with a software module that can control cochlear implants via a research interface box (RIB2, MEDEL GmbH, Innsbruck, Austria) were successfully performed. Using this module, electrical auditory steady state responses can be evaluated.
In our software, two substantially different statistical tests (Tcirc and F-test) are implemented. The first one, the Tcirc test, evaluates information from only a single frequency channel. This is advantageous because it assures independence from neighboring channels, which can contain disturbing signals. The disadvantage is low sensitivity for artificial signals (Fig. 4). In this case, for reliable test performance, subsequent measurements compensate for the missing information from neighboring channels. Consequently, the Tcirc test initially shows false positive test results. After one or several measurements, test responses are reliable. If the test signal contained phase alterations then the Tcirc test showed strong beating waves in the probability results because of its phase sensitivity (Fig. 4). In contrast, the F-test values showed only small beating waves. This disadvantage for artificial signals disappeared in tests with EEG signals (Figs. 5–7). Furthermore, the Tcirc test reached significance faster than the F-test. In the F-test, a number of neighboring channels (in this study n = 60) are compared to a single frequency channel. The information from the neighboring channels constitute an estimator for the noise in the EEG. Especially for artificial signals, the supplemental information from neighboring channels contributed to quickly reaching test significance and reliable responses at the beginning of the test (Fig. 4). For ASSR in normal hearing subjects, the Tcirc test may be superior (Figs. 6 and 7).
4.1. Phase coherence Our online recording and calibration tests showed precise and reliable responses of our setup after stimulation with acoustic stimuli. The sound card latency jitter may be a critical parameter for the online analysis because several recorded epochs must exactly match in their phase. We used a low latency external RME sound card (RME Fireface 400, Audio AG, Haimhausen, Germany) at a sampling rate of 44.1 kHz. We have not yet performed the test with built-in sound cards, but if the sampling rate is at least 44.1 kHz and latency jitter is low, reliable responses can be expected. In our software, the function ‘getdata’ from the Matlab Data Acquisition Toolbox (Version 2.12, R2008a, Matlab 7.6.0.324 R2008a, The MathWorks Inc.) is used. The manuals states that “in
4.3. Stability Hardware interfaces (data acquisition and headphone control), online FFT and online analysis, and diagram plotting in the graphical user interface showed fast performance with different test paradigms and were robust against errors. In contrast to tests using commercial devices, which sometimes produced software conflicts, our setup showed reliable performance in up to about 100 test runs.
5. Conclusions • A low-cost robust hardware and software environment in Matlab for ASSR was developed. • The phase of the modulated input signal was preserved in our hardware and software. • The two statistical tests (Tcirc and F-test) were sensitive to modulated signals.
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• The setup can be used for ASSR because the tests showed reliable responses to modulated signals. Acknowledgements We would like to thank Dr. J.D. Victor, Cornell University, NY, for assistance with the Tcirc test, two anonymous reviewers for their helpful comments, and Dr. Jane M. Opie for improvements to our English language usage. The work was supported by the grant from the DFG (BA 2085/3-1).
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Appendix A. A.1. Matlab code in main program for Tcirc and F-test The program code for the statistical test Tcirc (Victor and Mast, 1991) and F-test (e.g., John and Picton, 2000) are printed in the following. The used function “tcirc” is described below, the function “fcdf” is a function from the Matlab Statistics Toolbox (Version 6.2, R2008a). “fcdf” is the F cumulative distribution function. The number “loc” indicates the frequency channel that is tested.
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A.2. Matlab code for Tcirc function In the following the program code for the Tcirc function is printed. For more details see Section 1 and the paper from Victor and Mast (1991).
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