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Eeg Alpha Activity Processing and Modelling
EEG ALPHA ACTIVITY PROCESSING AND MODELLING P. Lansky, Z. Bohdanecky, M. Indra and T. RadiI Institute of Physiology, Czechoslovak Academy of Sciences, Prague, Czechoslovakia
Abstract. On-line as well as off-line EEG alpha activity evaluation is described. The methods of detection of EEG alpha activity and its scoring are mentioned. A filtered EEG activity is fed into a computer where by sequential approach the value of median of probability distribution of their local extremes has been estimated. This value serves as a threshold for the threshold detection itself. Integral detection method is based on the computation of the area under this filtered signal. Model of alpha and non-alpha alternation based upon the analogy with the queueing process is suggested. Keywords. BiocontrolJ electroencephalographYJ feedback; modellingJ selfcontrol. INTRODUCTION The human EEG alpha activity is one of the most important bioelectrical events of the human brain and it thus represents the center of broad interest. Despite this fact little attention has been paid to the description of this term and to exact methods of alpha activity detection (Dick and Vaughn, 1970; Bohdanecky and co-workers, 1978).
periods (presence or absence of alpha activity). These intervals stored in computer#s external memory are then statistically analyzed. Taking into account the assumption of random presence of alpha spindles the given process can be considered as a realization of alternating point process (the alpha - non-alpha transition or vice versa is, namely, imnediate). 'l.'he mentioned off-line system was programmed in BASIC RT and in ASSEMBLER on PDP 8/e minicomputer.
A. OFF-LIN~ ALPHA ACTIVITY hVALlJATION Two basic methods of alpha activity detection and scoring are to be described: 1. The threshold method and 2. the integral method. For both a 8-12 Hz band-pass filtering of the tested signal is required.
2. The integral alpha activity detection method is based on continuous computation of the area under the curve of the filtered EEG signal. The constant sampling frequency of 100 Hz was applied and the integral was estimated by a rectangular method. The length of integrated period and the number of integral scores needed for computation are to be chosen prior the tape play-back. The scores were then stored in the external memory for further statistical evaluation to check namely the stability of EEG alpha activity energetiC content.
1. The threshold method (for details see e.g. Dohdanecky and co-workers, 1977) is more complicated than the integral one. A 3-stage approach has been applied in this case. The histogram of local extremes of filtered ~~G signal was estimated by sampling the signal with the frequency of 100 samples/s and the last value of the monotonous sample was loaded into the memory. Then the median or other quantile could be used as threshold for alpha activity detection. The chosen threshold diviues the tested E~G signal during its second play-back into alternating sequences of alpha and non-alpha
The relationship between methods mentioned above has not been established yet, despite some first attempts (e.g. Hardt and Kamiya, 1976). Each method reflects an alpha activity pattern of its own; the integral method was found to be less sensitive 311
P . Lan s ky et a l .
3 12
to sharp dynamic changes in alpha acti vi ty amount. B. ON-LINE ALPHA ACTIVITY EVALUA'l'ION The E.i::G alpha activity feedback upon the subject (i.e. the possible selfregulation) is to be checked by online procedures. Filtered alpha activity is fed into TESLA JPR 12 minicompu ter and the local extremes histogram from the signal within preselected time i nterval is computed. In the next trial detection threshold based on previous information is set either by the experimentator himself or automatically. The subject is in this case informed (acoustically) about the alpha activity presence which corresponds to the alpha spindle above the threshold. In the third trial the feedback is replaced by an instruction for the subject so as to master the previous experience and to try to increase the alpha activity by EEG self-control. The alpha activity amount is registered continuously (see Fig. 1). C. AN ALPHA AND NON-ALPHA ALTERNATION MODEL One of the possibilities how to evaluate the alternating sequence of alpha and non-alpha periods is to find a suitable mathematical model. The hypothesis of two alternating states that correspond to the particular levels of arousal has already been considered in EEG modelling (see Walter and co-workers, 1967; Dubes and co-workers, 1969). In our model this hypothesis is presented in analogy with the queueing theory. It is assumed that alpha intervals correspond to idle periods of the M/G/l system and non-alpha intervals to its busy periods. Alpha activity thus corresponds to the stage of quiety phase, starts working when the first request appears and continues till the last waiting request is finished. We are aware that the model is simplified and can thus fit our experimental situation only (tne subject sitting with closed eyes in a dark, sound-attenuated chamber), as shown by Bohdanecky and co-workers (1978). The unsolved question of "non-alpha" per iods distribution remains. The system M/M/l was finally rejected when the simulation on PDP 8/e computer did not correspond the experimental data. This negative finding about randomness of one customer service time distribution is, however, in accordance with recent psychophysiological data on brain activity. The preliminary results as well as
the model mentioned above suggest that the feedback in this situation has a negative function: the total alpha activity amount is decreased (the information about the presence of alpha period can be considered as a customer's entry). Influence of the experience of negative feedback upon alpha activity self-control is a matter of further analysis. REFERENCES Bohdanecky, Z., P. Lansky, M. Indra and T. Radil-Weiss (1977). Dynamics of EEG alpha activity and non-alpha period alternation and vigilance control. IFAC Symposium, Leipzig, Vol. 2, 38-44. Bohdanecky, Z., P. Lansky, M. Indra and T. Radil-Weiss (1978). EEG alpha and non-alpha intervals alternation. Biol. Cybernetics, 30 109-113. DiC~D.E., and A.O. vaughn (1970). Mathematical description and computer detection of alpha waves. Mathematical Biosciences, 7, 81-95. Dubes, R.C., A. Hung, and W.R. McCrum (1969). Classification of the electroencephalograms with pattern recognition algorithms. Interim Scient. Re~ort No.4, Air Force OffIce of cl. Res., Arlington. Hardt, J.V. and J. Kamyia (1976). Conflicting results in EEG alpha feedback studies. Biofeedback and self-Re~ulationa 1, 63-75. wal~D.O.,.M. Rho es, and W.R. Adey (1976) Discrimi~atinq among states of consciousness by EEG measurements. A study of four subjects. Electroence~halo~r. clin. NeurophysIol., 7, 2 -29.
EEG Alpha activity processing and modelling
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313
STATISTICAL ANALYSIS
~ EXTERNAL CONTROL
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FEEDBACK SElFCONTROl
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AUDIO
L __________________________________________________ _
ON LINE OFF LINE
Fig. 1. Block scheme of the model describing EEG alpha and nonalpha period alternation
Abstract. On-line as well as off-line EEG alpha activity evaluation is described. The methods of detection of EEG alpha activity and its scoring are mentioned. A filtered EEG activity is fed into a computer where by sequential approach the value of median of probability distribution of their local extremes has been estimated. This value serves as a threshold for the threshold detection itself. Integral detection method is based on the computation of the area under this filtered signal. Model of alpha and non-alpha alternation based upon the analogy with the queueing process is suggested. Keywords. BiocontrolJ electroencephalographYJ feedback; modellingJ selfcontrol. INTRODUCTION The human EEG alpha activity is one of the most important bioelectrical events of the human brain and it thus represents the center of broad interest. Despite this fact little attention has been paid to the description of this term and to exact methods of alpha activity detection (Dick and Vaughn, 1970; Bohdanecky and co-workers, 1978).
periods (presence or absence of alpha activity). These intervals stored in computer#s external memory are then statistically analyzed. Taking into account the assumption of random presence of alpha spindles the given process can be considered as a realization of alternating point process (the alpha - non-alpha transition or vice versa is, namely, imnediate). 'l.'he mentioned off-line system was programmed in BASIC RT and in ASSEMBLER on PDP 8/e minicomputer.
A. OFF-LIN~ ALPHA ACTIVITY hVALlJATION Two basic methods of alpha activity detection and scoring are to be described: 1. The threshold method and 2. the integral method. For both a 8-12 Hz band-pass filtering of the tested signal is required.
2. The integral alpha activity detection method is based on continuous computation of the area under the curve of the filtered EEG signal. The constant sampling frequency of 100 Hz was applied and the integral was estimated by a rectangular method. The length of integrated period and the number of integral scores needed for computation are to be chosen prior the tape play-back. The scores were then stored in the external memory for further statistical evaluation to check namely the stability of EEG alpha activity energetiC content.
1. The threshold method (for details see e.g. Dohdanecky and co-workers, 1977) is more complicated than the integral one. A 3-stage approach has been applied in this case. The histogram of local extremes of filtered ~~G signal was estimated by sampling the signal with the frequency of 100 samples/s and the last value of the monotonous sample was loaded into the memory. Then the median or other quantile could be used as threshold for alpha activity detection. The chosen threshold diviues the tested E~G signal during its second play-back into alternating sequences of alpha and non-alpha
The relationship between methods mentioned above has not been established yet, despite some first attempts (e.g. Hardt and Kamiya, 1976). Each method reflects an alpha activity pattern of its own; the integral method was found to be less sensitive 311
P . Lan s ky et a l .
3 12
to sharp dynamic changes in alpha acti vi ty amount. B. ON-LINE ALPHA ACTIVITY EVALUA'l'ION The E.i::G alpha activity feedback upon the subject (i.e. the possible selfregulation) is to be checked by online procedures. Filtered alpha activity is fed into TESLA JPR 12 minicompu ter and the local extremes histogram from the signal within preselected time i nterval is computed. In the next trial detection threshold based on previous information is set either by the experimentator himself or automatically. The subject is in this case informed (acoustically) about the alpha activity presence which corresponds to the alpha spindle above the threshold. In the third trial the feedback is replaced by an instruction for the subject so as to master the previous experience and to try to increase the alpha activity by EEG self-control. The alpha activity amount is registered continuously (see Fig. 1). C. AN ALPHA AND NON-ALPHA ALTERNATION MODEL One of the possibilities how to evaluate the alternating sequence of alpha and non-alpha periods is to find a suitable mathematical model. The hypothesis of two alternating states that correspond to the particular levels of arousal has already been considered in EEG modelling (see Walter and co-workers, 1967; Dubes and co-workers, 1969). In our model this hypothesis is presented in analogy with the queueing theory. It is assumed that alpha intervals correspond to idle periods of the M/G/l system and non-alpha intervals to its busy periods. Alpha activity thus corresponds to the stage of quiety phase, starts working when the first request appears and continues till the last waiting request is finished. We are aware that the model is simplified and can thus fit our experimental situation only (tne subject sitting with closed eyes in a dark, sound-attenuated chamber), as shown by Bohdanecky and co-workers (1978). The unsolved question of "non-alpha" per iods distribution remains. The system M/M/l was finally rejected when the simulation on PDP 8/e computer did not correspond the experimental data. This negative finding about randomness of one customer service time distribution is, however, in accordance with recent psychophysiological data on brain activity. The preliminary results as well as
the model mentioned above suggest that the feedback in this situation has a negative function: the total alpha activity amount is decreased (the information about the presence of alpha period can be considered as a customer's entry). Influence of the experience of negative feedback upon alpha activity self-control is a matter of further analysis. REFERENCES Bohdanecky, Z., P. Lansky, M. Indra and T. Radil-Weiss (1977). Dynamics of EEG alpha activity and non-alpha period alternation and vigilance control. IFAC Symposium, Leipzig, Vol. 2, 38-44. Bohdanecky, Z., P. Lansky, M. Indra and T. Radil-Weiss (1978). EEG alpha and non-alpha intervals alternation. Biol. Cybernetics, 30 109-113. DiC~D.E., and A.O. vaughn (1970). Mathematical description and computer detection of alpha waves. Mathematical Biosciences, 7, 81-95. Dubes, R.C., A. Hung, and W.R. McCrum (1969). Classification of the electroencephalograms with pattern recognition algorithms. Interim Scient. Re~ort No.4, Air Force OffIce of cl. Res., Arlington. Hardt, J.V. and J. Kamyia (1976). Conflicting results in EEG alpha feedback studies. Biofeedback and self-Re~ulationa 1, 63-75. wal~D.O.,.M. Rho es, and W.R. Adey (1976) Discrimi~atinq among states of consciousness by EEG measurements. A study of four subjects. Electroence~halo~r. clin. NeurophysIol., 7, 2 -29.
EEG Alpha activity processing and modelling
G)
313
STATISTICAL ANALYSIS
~ EXTERNAL CONTROL
@ @
~-------
FEEDBACK SElFCONTROl
--- --- - ---
AUDIO
L __________________________________________________ _
ON LINE OFF LINE
Fig. 1. Block scheme of the model describing EEG alpha and nonalpha period alternation