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This paper presents an approach for gender recognition from single channel EEG signal. For this purpose, approximately 24 hour-long EEG data, obtained during daily routine activities including sleep, was used. First, cepstrum coefficients of EEG signals were obtained in the frequency domain to construct the features SET. Second, a machine learning step was performed using these features with Support...
In this study, binary sleep stage classification (sleep or awake state) was performed using single-channel EEG signal. A new frequency warping function is proposed for this purpose. This function provides a bending function that can proper orientation and depth of the EEG signal frequency content. In this way a generalized filter set of was designed. With the help of this filter set, cepstrum features...
Sleep stage binary classification is studied using single channel EEG signals. The proposed approach is composed of two steps. In the first step, cepstrum coefficients based features are obtained from EEG signals using a filter bank approach which is tuned for sleep stage classification in terms of number of filters and their type. In the second step, these features are used with support vector machine...
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