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To determine if behavioral states are associated with unique spatial electrocorticographic (ECoG) patterns, we obtained recordings with a microgrid electrode array applied to the cortical surface of a human subject. The array was constructed with the intent of extracting maximal spatial information by optimizing interelectrode distances. A 34-year-old patient with intractable epilepsy underwent intracranial ECoG monitoring after standard methods failed to reveal localization of seizures. During the 8-day period of invasive recording, in addition to standard clinical electrodes a square 1 × 1 cm microgrid array with 64 electrodes (1.25 mm separation) was placed on the right inferior temporal gyrus. Careful review of video recordings identified four extended naturalistic behaviors: reading, conversing on the telephone, looking at photographs, and face-to-face interactions. ECoG activity recorded with the microgrid that corresponded to these behaviors was collected and ECoG spatial patterns were analyzed. During periods of ECoG selected for analysis, no electrographic seizures or epileptiform patterns were present. Moments of maximal spatial variance are shown to cluster by behavior. Comparisons between conditions using a permutation test reveal significantly different spatial patterns for each behavior. We conclude that ECoG recordings obtained on the cortical surface with optimal high spatial frequency resolution reveal distinct local spatial patterns that reflect different behavioral states, and we predict that similar patterns will be found in many if not most cortical areas on which a microgrid is placed.

To determine if behavioral states are associated with unique spatial electrocorticographic (ECoG) patterns, we obtained recordings with a microgrid electrode array applied to the cortical surface of a human subject. The array was constructed with the intent of extracting maximal spatial information by optimizing interelectrode distances. A 34-year-old patient with intractable epilepsy underwent intracranial ECoG monitoring after standard methods failed to reveal localization of seizures. During the 8-day period of invasive recording, in addition to standard clinical electrodes a square 1 × 1 cm microgrid array with 64 electrodes (1.25 mm separation) was placed on the right inferior temporal gyrus. Careful review of video recordings identified four extended naturalistic behaviors: reading, conversing on the telephone, looking at photographs, and face-to-face interactions. ECoG activity recorded with the microgrid that corresponded to these behaviors was collected and ECoG spatial patterns were analyzed. During periods of ECoG selected for analysis, no electrographic seizures or epileptiform patterns were present. Moments of maximal spatial variance are shown to cluster by behavior. Comparisons between conditions using a permutation test reveal significantly different spatial patterns for each behavior. We conclude that ECoG recordings obtained on the cortical surface with optimal high spatial frequency resolution reveal distinct local spatial patterns that reflect different behavioral states, and we predict that similar patterns will be found in many if not most cortical areas on which a microgrid is placed.

Our objective was to examine if the high-density, 256 channel, scalp interictal EEG data can be used for localizing the epilepsy areas in patients. This was done by examining the long-range temporal correlations (LRTC) of EEGs and also that of the phase synchronization index (SI) of EEGs. It was found that the LRTC of scalp SI plots were better in localizing the seizure areas as compared with the LRTC of EEGs alone. The EEG data of one minute duration was filtered in the low Gamma band of 30-50 Hz. A detrended fluctuation analysis (DFA) was used to find LRTC of the scalp EEG data. Contour plots were constructed using a montage of the layout of 256 electrode positions. The SI was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The SI for each electrode was averaged over with the nearby six electrodes. LRTC of the SI was computed and spatial plots were made. It was found that the LRTC of SI was highest at the location of the epileptic sites. A similar pattern was not found in the LRTC of EEGs. This provides a noninvasive way to localize seizure areas from scalp EEG data.

Our objective was to examine if the high-density, 256 channel, scalp interictal EEG data can be used for localizing the epilepsy areas in patients. This was done by examining the long-range temporal correlations (LRTC) of EEGs and also that of the phase synchronization index (SI) of EEGs. It was found that the LRTC of scalp SI plots were better in localizing the seizure areas as compared with the LRTC of EEGs alone. The EEG data of one minute duration was filtered in the low Gamma band of 30-50 Hz. A detrended fluctuation analysis (DFA) was used to find LRTC of the scalp EEG data. Contour plots were constructed using a montage of the layout of 256 electrode positions. The SI was computed after taking Hilbert transform of the EEG data. The SI between a pair of channel was inferred from a statistical tendency to maintain a nearly constant phase difference over a given period of time even though the analytic phase of each channel may change markedly during that time frame. The SI for each electrode was averaged over with the nearby six electrodes. LRTC of the SI was computed and spatial plots were made. It was found that the LRTC of SI was highest at the location of the epileptic sites. A similar pattern was not found in the LRTC of EEGs. This provides a noninvasive way to localize seizure areas from scalp EEG data.

Mesoscopic patterns of neural activity were sought in multichannel EEGs of rabbits that were trained to respond to conditioned stimuli (CSs) in visual, auditory and somatic modalities. Spatiotemporal patterns were sought of oscillations in the beta and gamma ranges. The techniques required for preprocessing EEGs in search of global patterns were diametrically opposed to those needed for localization of modular EEG signals. Frames were found in the form of intermittent spatial patterns of phase and amplitude modulation (AM and PM) of carrier waves in beta and gamma ranges that served to classify EEG frames with respect to CSs. A model based on the intentional action-perception cycle is proposed to complement the information processing model.

Mesoscopic patterns of neural activity were sought in multichannel EEGs of rabbits that were trained to respond to conditioned stimuli (CSs) in visual, auditory and somatic modalities. Spatiotemporal patterns were sought of oscillations in the beta and gamma ranges. The techniques required for preprocessing EEGs in search of global patterns were diametrically opposed to those needed for localization of modular EEG signals. Frames were found in the form of intermittent spatial patterns of phase and amplitude modulation (AM and PM) of carrier waves in beta and gamma ranges that served to classify EEG frames with respect to CSs. A model based on the intentional action-perception cycle is proposed to complement the information processing model.

We evaluated the rapid changes in regional scalp EEG synchronization in normal subjects with spatial and temporal resolution exceeding prior art 10-fold with a high spatial density array and the Hilbert transform. A curvilinear array of 64 electrodes 3 mm apart extending 18.9 cm across the scalp was used to record EEG at 200/sec. Analytic amplitude (AA) and phase (AP) were calculated at each time step for the 64 traces in the analog pass band of 0.5-120 Hz. AP differences approximated the AP derivative (instantaneous frequency). The AP from unfiltered EEG revealed no reproducible patterns. Filtering was necessary in the beta and gamma ranges according to a technique that optimized the correlation of the AP differences with the activity band pass filtered in the alpha range. The sizes of temporal AP differences were usually within +/-0.5 radian from the average step corresponding to the center frequency of the pass band. Large AP differences were often synchronized over distances of 6 to 19 cm. An optimal pass band to detect and measure these recurring jumps in AP in the beta and gamma ranges was found by maximizing the alpha peak in the cospectrum of the correlation between unfiltered EEG and the band pass AP differences. Synchronized AP jumps recurred in clusters (CAP) at alpha and theta rates in resting subjects and with EMG. Cortex functions by serial changes in state. The Hilbert transform of EEG from high-density arrays can visualize these state transitions with high temporal and spatial resolution and should be useful in relating EEG to cognition.

We evaluated the rapid changes in regional scalp EEG synchronization in normal subjects with spatial and temporal resolution exceeding prior art 10-fold with a high spatial density array and the Hilbert transform. A curvilinear array of 64 electrodes 3 mm apart extending 18.9 cm across the scalp was used to record EEG at 200/sec. Analytic amplitude (AA) and phase (AP) were calculated at each time step for the 64 traces in the analog pass band of 0.5-120 Hz. AP differences approximated the AP derivative (instantaneous frequency). The AP from unfiltered EEG revealed no reproducible patterns. Filtering was necessary in the beta and gamma ranges according to a technique that optimized the correlation of the AP differences with the activity band pass filtered in the alpha range. The sizes of temporal AP differences were usually within +/-0.5 radian from the average step corresponding to the center frequency of the pass band. Large AP differences were often synchronized over distances of 6 to 19 cm. An optimal pass band to detect and measure these recurring jumps in AP in the beta and gamma ranges was found by maximizing the alpha peak in the cospectrum of the correlation between unfiltered EEG and the band pass AP differences. Synchronized AP jumps recurred in clusters (CAP) at alpha and theta rates in resting subjects and with EMG. Cortex functions by serial changes in state. The Hilbert transform of EEG from high-density arrays can visualize these state transitions with high temporal and spatial resolution and should be useful in relating EEG to cognition.