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Optimal kernel time-frequency representation of EEG signals in patients with spinal cord injury

Lakany, H. (2006) Optimal kernel time-frequency representation of EEG signals in patients with spinal cord injury. In: Society for Neuroscience Annual meeting 2006, 2006-10-14.

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The goal of this study is to develop signal processing methods that can be used to enhance visualization and feature extraction of EEG signals for use in developing brain-computer interfaces (BCIs) for patients with spinal-cord injury (SCI). In this work, a method to represent EEG signals in the time-frequency domain by designing an optimal kernel function which allows for the extraction of maximum possible time-frequency information. The study is based on EEG data recorded from 3 SCI patients attempting to perform 30sec periods of sustained voluntary contractions of the muscles affected by the SCI. Testing protocols were approved by a local Ethics Committee. 30 channels of EEG were recorded together with wrist and ankle muscle EMG when the patient was relaxed and during periods when the patient attempted to generate a sustained wrist extension or ankle dorsi-flexion. Data was collected from each patient on two separate occasions at a minimum interval of 10 weeks. The method we propose here is based on Wigner-Ville distribution which has proved superior to short-time Fourier and Wavelet transforms in visualizing the energy distribution in time-frequency domain. From the time-frequency representation proposed, we extracted information such as moments and marginals and identified the number of elementary sources contributing to the EEG signal during the attempted movement. For each subject, estimated moments and marginals were similar for each test day. This is interpreted as an indication that the extracted signal associated with the performance of an attempted voluntary contraction is relatively stable and can be considered independent of any spatio-temporal reorganization that may have happened in the motor system in response to a spinal lesion in the interval between test sessions. We conclude that this information may potentially be considered as salient features in developing non-invasive BCI applications for SCI patients.