Emotion recognition from single-channel EEG signals using a two-stage correlation and instantaneous frequency-based filtering method. (May 2019) - Taran, Sachin
Highlights: Audio-video stimulus based EEG-dataset of four emotions is recorded. A new CIF-based filtering method is proposed for EEG signals. The proposed method provides better emotion classification results compared to other state-of-the-art approaches. The proposed method classifies emotions using single-bipolar EEG channel, which can greatly reduce the complexity of emotion-recognition based BCI systems. Abstract: Background and objective: The recognition of emotional states is a crucial step in the development of a brain-computer interface (BCI) system. Emotion recognition system finds applications in medical science for the impaired and disabled people. Electroencephalography assesses the neurophysiology of the brain for recognition of different emotional states. Methods: The audio-video stimulus based experimental setup is arranged for the electroencephalogram (EEG) recordings of happy, fear, sad, and relax emotions and a two-stage filtering method is proposed for the recognition of emotion EEG signals. At the first stage, a correlation-criterion is suggested for removal of noisy intrinsic mode functions (IMFs) from the IMFs obtained by applying the empirical mode decomposition on the raw EEG signal. The noise-free IMFs are used to reconstruct the denoised EEG signal with improved stationarity characteristics. The denoised EEG signal is further decomposed into modes using the variational mode decomposition (VMD). At the second stage, the instantaneous-frequency based filtering of VMD modes is performed and filtered modes are retained for the reconstruction of denoised EEG signal with the desired frequency range. After two-stage filtering, the non-linear measures of filtered EEG signals are used as input features to multi-class least squares support vector machine (MC-LS-SVM) classifier for emotion recognition. Results: The different kernel functions are tested in MC-LS-SVM classifier for emotion recognition. The Morlet wavelet (MW) kernel function provides the best individual classification accuracies for happy, fear, sad, and relax emotions as 92.79%, 87.62%, 88.98%, and 93.13%, respectively. The MW-kernel function also obtained the best overall accuracy of 90.63%, F1-score 0.9064, and kappa value 0.8751. Conclusions: The Audio-video stimulus based emotion EEG-dataset is recorded. A new filtering method is proposed for EEG signals. The proposed method provides better emotion recognition performance as compared to the state-of-the-art methods and classifies emotions using single-bipolar EEG channel, which can greatly reduce the complexity of emotion-recognition based BCI systems.