- Email: [email protected]
Intersite coherences for enhanced spatial resolution EEG data (scalp- and cortical-surface potentials and Laplacians)
28P
Society Proceedings/Electroencephalography
68. A seizure detector based on a neural network for EEG recordings from scalp. - W.R.S. Webber, R.T. Richardson and RP. Lesser (Department of Neurology and Zanvyl Krieger Mind Brain Institute, Johns Hopkins University, Baltimore, MD) We have developed a neural network EEG seizure detector. The input layer has 31 nodes quantifying the amplitude, slope, curvature, rhythmicity and frequency components of EEG in a 2 set epoch. The hidden layer has 30 nodes and the output layer has 8 nodes representing various patterns of EEG activity (e.g., seizure, muscle, noise, normal). The value of the output node representing large seizure activity is averaged over 3 consecutive epochs. A seizure is declared when that average exceeds 0.65. The network was trained on epochs from 16 EEG files from 13 patients. Ten files contained verified seizures. The training set contained 1000 samples and the test set contained 500 samples of each of the 8 categories of EEG activity. Among 81 randomly selected files from 50 patients not in the original training, the detector declared at least 1 seizure in 76% of 37 files containing seizures. It declared no seizures in 93% of 44 files not containing seizures. Five false detections during 4.1 h of recording yielded a false detection rate of 1.2/h. This performance is comparable to rule-based detectors for scalp recorded EEG activity (Gotman, 1990; Pauri et al., 1992). Performance enhancements may result from enlarging the training set and creating separate networks for specific conditions such as complex partial seizures or childhood epilepsies. Dr. Webber is a consultant
to Biologic
Systems Corporation.
69. The prediction of the epileptic seizure event as manifested in EEG wave forms. - J. Beetem, J. Benedetto, M. BozekKuzmicki, D. Colella, J. Creekmore, M. Dellomo, G. Jacyna and G. Pei (The MfTRE Corporation, McLean, VA) A group at the MITRE Corporation has been investigating the possibility of predicting the onset of seizure activity as manifested in electroencephalogram wave forms recorded from subdural electrodes. Our initial efforts concentrated primarily on 3 predictor schemes: auto-regressive modeling, a pulse-amplitude modulation model, and a neural network-based approach. In some cases our methods are able to provide a 2 set prediction of the epileptic event. These techniques require supervised training of the prediction algorithms in order to develop feature sets that can distinguish a seizure event from a non-seizure event. The algorithms have been tested for robustness relative to electrode sensors and time differences. Further research into the application of non-linear predictors is ongoing. 70. The use of the wavelet transform to compress electrophysiological data. - R.D. Sidman and C.H. Chu ’ (Department of Mathematics and ’ Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette, LA) Neurologists at the Mayo Clinic Foundation and Cleveland Clinic Foundation (e.g., Lagerlund and Jacobs) are developing a public archive of digitized EEG signals. These datasets would eventually be available, via the Internet, to researchers who are investigating various methods for analyzing such signals. A 5 min EEG epoch sampled at 200 Hz required 22 megabytes of storage and took > 30 min to transfer using the file transfer protocol, for example. There is, therefore, ample motivation to compress and streamline such datasets. The principle behind the compression of these data is: Suppose discretized wave form data are expanded in the form, x(n) = Z:,c,@,(n). The idea is to keep a selected subset of the coefficients as the compressed form, which can then be used to reconstruct an approximation of x(n). The choice of the basis set, I@,,}, will determine how many coefficients are needed for minimizing the reconstruction error.
and clinical Neurophysiolo&~ 95 (1995) 15P-41P We will derive the basis set from the so-called 12.point Daubechies wavelet and apply the wavelet transform to the compression of (I) a sequence of electrocardiographic data with a sharp transient (such as found in EEG data), and (21 a single beam of echocardiographic data. Possible applications to EEG data compression will be discussed.
71. Movement related coherence change and phase-locking of sensorimotor cortex in human electrocorticogram. - M. Keidel, C. Toro, S. Sato ‘, C. Kufta b and M. Hallett (Human Motor Control Section, Medical Neurology Branch, ’ EEG Laboratory and b Surgical Neurology Branch, National Institutes of Health, Bethesda, MD)
We analyzed the changes of coherence and phase-locking of oscillatory surface field potentials in the sensorimotor cortex in relation to voluntary movement. In 6 seizure patients with subdural grids overlying the sensorimotor cortex, the electrocorticogram (ECOG) was recorded from 15 electrodes over an area of 3 X 5 cm’. Direct cortical stimulation data, the topography of readiness potentials, spectral densities and SEPs were obtained. Movement related changes in coherence and phase with respect to the electrode over the hand motor cortex were analyzed with respect to contralateral self-paced index finger movements. Changes in the coherence before and during movement were evident in the theta, alpha, beta and gamma bands including 40 Hz. Typically, coherence increased except between those electrodes with initial high coherence, where coherence remained stable or decreased. Time of onset and slope of coherence changes varied with the anatomical relation of the recording electrodes. At the time of movement onset coherence was characterized by a zero phase-locking or crossing with a reversal of the phase. The data suggest neuronal synchronization in the sensorimotor cortex as a basic principle of sensorimotor integration which shows similarities to that observed in the visual system (Gray et al., 1989).
72. Intersite coherences for enhanced spatial resolution EEG data (scalp- and cortical-surface potentials and Laplacians). - T.D. Lagerlund, F.W. Sharbrough, N.E. Busacker and K. Cicora (Section of Electroencephalography, Mayo Clinic, Rochester, MN)
Interchannel coherence is a measure of spatial extent of and timing relationships between cerebral EEG generators. However, interchannel coherence of referentially recorded scalp potentials includes components due to volume conduction between sites and reference site activity. If the EEG were transformed to reference-independent form and “smearing” effects of volume conduction were removed, interchannel coherences for the resulting “enhance spatial resolution” (ESR) EEG should be reduced. We compared interchannel coherences for 4 types of ESREEGs: (1) scalp-surface potentials using an average reference montage (SSP); (2) scalp-surface Laplacians (SSL); (3) cortical surface potentials estimated by solving an “inward continuation” problem (CSP); (4) cortical-surface Laplacians (CSL). The last three were calculated using the Spherical Harmonic Expansion method, with a 3concentric sphere volume conductor model. Interchannel coherence for simulated EEG (random data mixed with a common 10 Hz signal) was similar to unprocessed data in O-4, 4-8 and 13-20 Hz frequency bands; in the 8-13 Hz band, coherence was markedly less (0.040 f 0.013 for SSP, 0.071 + 0.083 for SSL, 0.072 + 0.086 for CSP, and 0.104 + 0.127 for CSL) than for unprocessed data (0.410 + 0.0241, due to removal of the common referential signal. Interchannel coherences of background EEG and partial seizure activity were less for ESREEGs other than SSP than for unprocessed recordings; CSP and SSL had the smallest coherences.
Society Proceedings/Electroencephalography
68. A seizure detector based on a neural network for EEG recordings from scalp. - W.R.S. Webber, R.T. Richardson and RP. Lesser (Department of Neurology and Zanvyl Krieger Mind Brain Institute, Johns Hopkins University, Baltimore, MD) We have developed a neural network EEG seizure detector. The input layer has 31 nodes quantifying the amplitude, slope, curvature, rhythmicity and frequency components of EEG in a 2 set epoch. The hidden layer has 30 nodes and the output layer has 8 nodes representing various patterns of EEG activity (e.g., seizure, muscle, noise, normal). The value of the output node representing large seizure activity is averaged over 3 consecutive epochs. A seizure is declared when that average exceeds 0.65. The network was trained on epochs from 16 EEG files from 13 patients. Ten files contained verified seizures. The training set contained 1000 samples and the test set contained 500 samples of each of the 8 categories of EEG activity. Among 81 randomly selected files from 50 patients not in the original training, the detector declared at least 1 seizure in 76% of 37 files containing seizures. It declared no seizures in 93% of 44 files not containing seizures. Five false detections during 4.1 h of recording yielded a false detection rate of 1.2/h. This performance is comparable to rule-based detectors for scalp recorded EEG activity (Gotman, 1990; Pauri et al., 1992). Performance enhancements may result from enlarging the training set and creating separate networks for specific conditions such as complex partial seizures or childhood epilepsies. Dr. Webber is a consultant
to Biologic
Systems Corporation.
69. The prediction of the epileptic seizure event as manifested in EEG wave forms. - J. Beetem, J. Benedetto, M. BozekKuzmicki, D. Colella, J. Creekmore, M. Dellomo, G. Jacyna and G. Pei (The MfTRE Corporation, McLean, VA) A group at the MITRE Corporation has been investigating the possibility of predicting the onset of seizure activity as manifested in electroencephalogram wave forms recorded from subdural electrodes. Our initial efforts concentrated primarily on 3 predictor schemes: auto-regressive modeling, a pulse-amplitude modulation model, and a neural network-based approach. In some cases our methods are able to provide a 2 set prediction of the epileptic event. These techniques require supervised training of the prediction algorithms in order to develop feature sets that can distinguish a seizure event from a non-seizure event. The algorithms have been tested for robustness relative to electrode sensors and time differences. Further research into the application of non-linear predictors is ongoing. 70. The use of the wavelet transform to compress electrophysiological data. - R.D. Sidman and C.H. Chu ’ (Department of Mathematics and ’ Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette, LA) Neurologists at the Mayo Clinic Foundation and Cleveland Clinic Foundation (e.g., Lagerlund and Jacobs) are developing a public archive of digitized EEG signals. These datasets would eventually be available, via the Internet, to researchers who are investigating various methods for analyzing such signals. A 5 min EEG epoch sampled at 200 Hz required 22 megabytes of storage and took > 30 min to transfer using the file transfer protocol, for example. There is, therefore, ample motivation to compress and streamline such datasets. The principle behind the compression of these data is: Suppose discretized wave form data are expanded in the form, x(n) = Z:,c,@,(n). The idea is to keep a selected subset of the coefficients as the compressed form, which can then be used to reconstruct an approximation of x(n). The choice of the basis set, I@,,}, will determine how many coefficients are needed for minimizing the reconstruction error.
and clinical Neurophysiolo&~ 95 (1995) 15P-41P We will derive the basis set from the so-called 12.point Daubechies wavelet and apply the wavelet transform to the compression of (I) a sequence of electrocardiographic data with a sharp transient (such as found in EEG data), and (21 a single beam of echocardiographic data. Possible applications to EEG data compression will be discussed.
71. Movement related coherence change and phase-locking of sensorimotor cortex in human electrocorticogram. - M. Keidel, C. Toro, S. Sato ‘, C. Kufta b and M. Hallett (Human Motor Control Section, Medical Neurology Branch, ’ EEG Laboratory and b Surgical Neurology Branch, National Institutes of Health, Bethesda, MD)
We analyzed the changes of coherence and phase-locking of oscillatory surface field potentials in the sensorimotor cortex in relation to voluntary movement. In 6 seizure patients with subdural grids overlying the sensorimotor cortex, the electrocorticogram (ECOG) was recorded from 15 electrodes over an area of 3 X 5 cm’. Direct cortical stimulation data, the topography of readiness potentials, spectral densities and SEPs were obtained. Movement related changes in coherence and phase with respect to the electrode over the hand motor cortex were analyzed with respect to contralateral self-paced index finger movements. Changes in the coherence before and during movement were evident in the theta, alpha, beta and gamma bands including 40 Hz. Typically, coherence increased except between those electrodes with initial high coherence, where coherence remained stable or decreased. Time of onset and slope of coherence changes varied with the anatomical relation of the recording electrodes. At the time of movement onset coherence was characterized by a zero phase-locking or crossing with a reversal of the phase. The data suggest neuronal synchronization in the sensorimotor cortex as a basic principle of sensorimotor integration which shows similarities to that observed in the visual system (Gray et al., 1989).
72. Intersite coherences for enhanced spatial resolution EEG data (scalp- and cortical-surface potentials and Laplacians). - T.D. Lagerlund, F.W. Sharbrough, N.E. Busacker and K. Cicora (Section of Electroencephalography, Mayo Clinic, Rochester, MN)
Interchannel coherence is a measure of spatial extent of and timing relationships between cerebral EEG generators. However, interchannel coherence of referentially recorded scalp potentials includes components due to volume conduction between sites and reference site activity. If the EEG were transformed to reference-independent form and “smearing” effects of volume conduction were removed, interchannel coherences for the resulting “enhance spatial resolution” (ESR) EEG should be reduced. We compared interchannel coherences for 4 types of ESREEGs: (1) scalp-surface potentials using an average reference montage (SSP); (2) scalp-surface Laplacians (SSL); (3) cortical surface potentials estimated by solving an “inward continuation” problem (CSP); (4) cortical-surface Laplacians (CSL). The last three were calculated using the Spherical Harmonic Expansion method, with a 3concentric sphere volume conductor model. Interchannel coherence for simulated EEG (random data mixed with a common 10 Hz signal) was similar to unprocessed data in O-4, 4-8 and 13-20 Hz frequency bands; in the 8-13 Hz band, coherence was markedly less (0.040 f 0.013 for SSP, 0.071 + 0.083 for SSL, 0.072 + 0.086 for CSP, and 0.104 + 0.127 for CSL) than for unprocessed data (0.410 + 0.0241, due to removal of the common referential signal. Interchannel coherences of background EEG and partial seizure activity were less for ESREEGs other than SSP than for unprocessed recordings; CSP and SSL had the smallest coherences.