Malihe Sabeti; Reza Boostani; Ehsan Moradi
Volume 13, Issue 4 , December 2019, , Pages 291-301
Abstract
The P300 event-related potentials (ERPs) has implicated in outcome evaluation and reward processing. It is controversial how reward processing affects the neural sources of P300. We try to investigate the effect of feedback on the neural sources of P300 component. Thirty healthy subjects were participated ...
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The P300 event-related potentials (ERPs) has implicated in outcome evaluation and reward processing. It is controversial how reward processing affects the neural sources of P300. We try to investigate the effect of feedback on the neural sources of P300 component. Thirty healthy subjects were participated and their EEG signals were recorded by thirty channels through the start (30 minutes), feedback (60-90 minutes) and last (30 minutes) segments. We analyzed feedback segment where an equal number of audio and visual stimulus were applied to the participants to perform audio and visual recognition tasks. We punished participants for wrong answers where each wrong answer adds four more tests to this segment. The P300 component was extracted from the background EEG at all channels using the conventional time-locked synchronous grand averaging over all time frames and subjects. Next, two well-known source localization algorithms including standardize low resolution electromagnetic tomography (sLORETA) and shrinking sLORETA were applied to the P300 waveforms for estimating the activity of the P300 sources. Our finding show a significant increase in the activation of P300 sources in the feedback-locked compared to the stimulus-locked over right tempo-parieto-occipital areas (secondary association area) in visual recognition task, but difference of P300 sources is not significant in audio recognition task. The discrepancy between the audio and visual task confirms the hypothesis that our participants considered more probability of wrong answers in the audio task, but they respond to visual test with more confidence.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Amin Zare; Reza Boostani; Mansour Zolghadr Jahromi
Volume 4, Issue 3 , June 2010, , Pages 195-208
Abstract
There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature ...
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There is a growing interest to improve seizure prediction by online analyzing of electroencephalogram (EEG) signals in epileptic patients. Seizure attack is occurred infrequently and unpredictably; hence, automatic detection of seizure during long-term is highly recommended. In this paper a novel Feature Reduction method namely AIS-RCA which adopted from the immunity system is proposed to improve the seizure detection rate. The automatic seizure detection can be performed in two successive stages: 1) The feature extraction/selection stage from EEG signals and 2) classifying the feature vectors by an efficient classifier. In this study, first, pseudo-Wigner-Ville distribution was applied to each window of the EEG signals and then the extracted features were transformed by AIS-RCA transform to represent the features in a more separable space. The AIS-RCA transformation matrix is estimated by using chunklets (a chunklet is defined as a subset of points that are known to be same). AIS-RCA using the proposed Artificial Immune System algorithm named Adaptive Distance-AIRS to discover the chunklets in the data space. Finally KNN classifier was applied to the transformed features to classify the seizure and non-seizure windows. The experimental results show that the proposed method yields epileptic detection accuracy rate up to 99.9% which is better than the results achieved by other types of features such as FFT, Wavelet transform, entropy and chaotic measures.
