# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
###### Abstract  
Multiclass motor imagery classification is essential for brain-computer interface systems such as prosthetic arms. The compressive sensing of EEG helps classify brain signals in real-time, which is necessary for a BCI system. However, compressive sensing is limited, despite its flexibility and data efficiency, because of its sparsity and high computational cost in reconstructing signals. Although the constraint of sparsity in compressive sensing has been addressed through neural networks, its signal reconstruction remains slow, and the computational cost increases to classify the signals further. Therefore, we propose a 1D-Convolutional Residual Network that classifies EEG features in the compressed (sparse) domain without reconstructing the signal. First, we extract only wavelet features (energy and entropy) from raw EEG epochs to construct a dictionary. Next, we classify the given test EEG data based on the sparse representation of the dictionary. The proposed method is computationally inexpensive, fast, and has high classification accuracy as it uses a single feature to classify without preprocessing. The proposed method is trained, validated, and tested using multiclass motor imagery data of 109 subjects from the PhysioNet database. The results demonstrate that the proposed method outperforms state-of-the-art classifiers with 96.6% accuracy. compressive sensing; sparse representation; motor imagery EEG classification; 1D-CNN; residual networks +
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signal-to-noise ratio. After preprocessing the signals, many feature extracting techniques, such as spatial and bandpass filtering, were used to extract the signals [4]. Later, these extracted features were used for training and testing the classification model. We have to extract these features in both the training and testing phases. The classification accuracies are compromised because of the non-stationarity of brain responses in MI-related tasks. Therefore, for a real-time BCI system, the input features must be invariant to data variations caused by the non-stationarity of the signals. Additionally, we have to develop classification algorithms that can track these changes [4]. The CS framework applied to a wearable EEG-based BCI [5] is energy-efficient and can perform real-time wireless data transmission, signal acquisition, and signal processing. In a CS-based BCI system, the signals are compressed as a linear superposition of a few coefficients at the transmission end. Following that, these sparse coefficients are used for signal reconstruction at the receiver end, followed by feature extraction and classification (see Figure 1). However, the reconstruction of the signal is computationally expensive and complex. A survey of recovery algorithms in compressive sensing is presented in [6], which mentions each algorithm's computational complexity in reconstructing the signal at the receiver end [1, 7, 8]. The classification of EEG signals is essential for an MI-based BCI system. Therefore, the sparsity-based classification system is efficient for an MI-based BCI system, eliminating the signal reconstruction and feature extraction at the receiver end [9]. This method reduces the memory required to store the reconstructed signal, lowers the computation cost, and develops a real-time prediction system. Researchers refer to such sparse representation-based classification (SRC) algorithms as 'compressive feature learning' algorithms [1]. First, the signals are represented as a sparse linear combination of extracted features from a dictionary, either predefined in the literature or learned from the data. Research is limited on dictionary learning and sparse representations compared with predefined dictionaries [10]. Next, state-of-the-art pattern recognition classifiers were used by researchers to classify these sparse features, such as support vector machines (SVMs) and linear discriminant analysis (LDA) classifiers [10, 11, 12].