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- Abstract: Brain-computer interfaces (BCI) are systems that link the brain with machines using brainwaves as a medium of communication using electroencephalography to explore the brain activity which is an affordable solution, noninvasive, easy setup, and portability. However, the neural signals are noisy, non-stationary, and nonlinear where the processing of those signals in a pattern recognition problem needs a complex pipeline of preprocessing, feature extraction, and classification algorithms that need an apriori knowledge to avoid compatibility issues and a deep understanding of the studied signals. Moreover, some techniques need a huge computational power on the CPU and a huge size of RAM. Therefore, several papers proposed to use Deep Learning to get state of the art performance and visualization of the learned features to have more understanding about the neural signals. But, the convolutional neural network (Convnet) are not used properly and the results are often random when we reproduced the works. Hence, we propose a combination of the discrete wavelet transform (DWT) and a Convnet that processes raw EEG data. The DWT will be used to reduce the size of the data without losing useful information. Also, a modified version of EEGNET will be used to extract the features and classification.
- Keywords: BCI, EEG, Convnet, DWT, WEEGNET