Browsing by Author "Nisar, Humaira"

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Convolutional neural networks for classification of music-listening EEG: comparing 1D convolutional kernels with 2D kernels and cerebral laterality of musical influence
(2020-07-01)
Cheah, Kit Hwa
;
Nisar, Humaira
;
Yap, Vooi Voon
;
Lee, Chen-Yi
;
電子工程學系及電子研究所
This paper highlights the ability of convolutional neural networks (CNNs) at classifying EEG data listening to different kinds of music without the requirement for handcrafted features. Deep learning architectures presented in this paper include CNN of different depths and different convolutional kernels. Support vector machine (SVM) taking in EEG features describing the frequency spectrum, signal regularity, and cross-channel correlation has been applied for performance comparison with CNN. The best performing CNN model presented in this paper achieves the tenfold cross-validation (CV) binary classification average accuracy of 98.94% (validation) and 97.46% (test), and the tenfold CV three-class classification accuracy of 97.68% (validation) and 95.71% (test). In comparison, the SVM classifier achieves tenfold CV binary classification accuracy of 80.23% (validation). The CNN model presented is able to not only differentiate EEG of subjects listening to music from that of subjects without auditory input, but it is also capable of accurately differentiating the EEG of subjects listening to different music. In the context of designing neural computing models for EEG analysis, this paper shows that decomposing two-dimensional spatiotemporal convolutional kernels into separate one-dimensional spatial and one-dimensional temporal kernels significantly reduces the number of trainable parameters (size) of the model while retaining the classification performance. This finding is useful, especially in designing CNN for memory-critical embedded systems for EEG processing. In neurological aspect, auditory stimulus is found to have altered the EEG pattern of the frontal lobe and the left cerebral hemisphere more than the other brain regions.
Short-time-span EEG-based personalized emotion recognition with deep convolutional neural network
(2019-01-01)
Cheah, Kit Hwa
;
Nisar, Humaira
;
Yap, Vooi Voon
;
Lee, Chen-Yi
;
電子工程學系及電子研究所
Emotion recognition can be useful in various applications such as in neurofeedback training for functional enhancement. A practically realizable emotion recognition system should rely on as little physiological signals/modalities as possible. Also, emotion-related neurological activities may be vastly different from person to person. Hence, this paper presents the single-modal EEG-based personalized emotion recognition convolutional neural network (CNN) models working on the DEAP dataset. The valence and arousal level classification performance of our presented CNN classifiers have surpassed the other emotion classifiers working on the DEAP dataset based on our scope of literature reviewed. The models, which are deep CNN, rely on only plain EEG data and require no pre-extracted EEG features. The design and application of the CNN models is aimed at possible future work of identification of new emotion-related EEG features, relying on the automated feature extraction capability of the CNN. The two CNN models presented have achieved the 3-class valence classification test accuracy of 97.59% and 98.75% respectively, and the 3-class arousal classification test accuracy of 98.48% and 97.58%.