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• W4205897305 abstract "Modern machine learning techniques require a huge amount of training data for satisfactory performance. This limits the application of these techniques in classifying EEG signals due to the non-availability of huge datasets. One way to mitigate this problem is by using data augmentation techniques to increase the number of training samples. In this paper, a novel common spatial pattern (CSP) based data augmentation technique is proposed. The efficiency of the proposed method is demonstrated by training a deep neural network (DNN) on the augmented dataset for decoding imagined speech from EEG. Using CSP, nine EEG channels that best represent the underlying cortical activity corresponding to the imagination of the words “in” and “cooperate” are identified, and discrete wavelet transform (DWT) features are extracted for each of these channels. Treating the selected EEG corresponding to each imagined word as an independent sample helps in providing enough samples to train the DNN. Maximum voting is applied to the results of individual feature vectors of each trial to obtain the predicted class label. We have obtained accuracies exceeding change level accuracies across subjects, which indicates that the network is able to generalize well." @default.
• W4205897305 created "2022-01-26" @default.
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• W4205897305 date "2021-07-09" @default.
• W4205897305 modified "2023-10-02" @default.
• W4205897305 title "Common Spatial Pattern Based Data Augmentation Technique for Decoding Imagined Speech" @default.
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• W4205897305 doi "https://doi.org/10.1109/conecct52877.2021.9622684" @default.
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