Journal of neuroscience methods · 2014
Independent component analysis-based method for electroencephalogram analysis during photic stimulation.
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Ferreira Danton Diego, Mendes Thais Martins, de Seixas José Manoel, Cerqueira Augusto Santiago, Miranda de Sá Antonio Mauricio Ferreira Leite
Аннотация
BACKGROUND: Intermittent photic stimulation (IPS) leads to phase- and time-locked activities in the electroencephalogram (EEG). While the first are easily obtained by means of averaging techniques (the evoked response), this latter reflects changes in the ongoing EEG that are important in event-related synchronization/desynchronization (ERS/ERD) studies. Techniques have been proposed for assessing such changes but they only provide the spectral estimate of the time-locked activities.
NEW METHOD: An independent component analysis-based method is proposed for the EEG analysis during IPS. Artificially generated sinusoidal functions at the stimulation frequency and harmonics are used together with the acquired EEG signal to build the observed vector that is presented to the ICA algorithm.
RESULTS: The proposed method was evaluated with simulated data and applied to EEG signals acquired on two electrodes placed over the occipital region showing that this method is capable of providing a suitable estimation of the ongoing EEG activities in both time and frequency domains.
COMPARISON WITH EXISTING METHOD(S): The results were compared with a coherence-based method, showing that the proposed method can estimate the power spectrum of the ongoing EEG activity as precisely as the coherence-based method with the advantage of allowing the ongoing EEG activity in time domain to be also obtained.
CONCLUSIONS: The application of the proposed method could be used for ERS/ERD studies, since it separates evoked responses, which are phase-locked to the stimuli, from those that change the ongoing EEG in a time-locked manner to the external stimulation.
NEW METHOD: An independent component analysis-based method is proposed for the EEG analysis during IPS. Artificially generated sinusoidal functions at the stimulation frequency and harmonics are used together with the acquired EEG signal to build the observed vector that is presented to the ICA algorithm.
RESULTS: The proposed method was evaluated with simulated data and applied to EEG signals acquired on two electrodes placed over the occipital region showing that this method is capable of providing a suitable estimation of the ongoing EEG activities in both time and frequency domains.
COMPARISON WITH EXISTING METHOD(S): The results were compared with a coherence-based method, showing that the proposed method can estimate the power spectrum of the ongoing EEG activity as precisely as the coherence-based method with the advantage of allowing the ongoing EEG activity in time domain to be also obtained.
CONCLUSIONS: The application of the proposed method could be used for ERS/ERD studies, since it separates evoked responses, which are phase-locked to the stimuli, from those that change the ongoing EEG in a time-locked manner to the external stimulation.