Motor imaginary (MI) deals with the movement of many parts of the body obtained from sensory-motor cortex activation. using BCI and some algorithms lead to the classification of EEG signal characteristics behaviors or pattern as well as design feedback to enable real-time or single-trial basis, a lot of technique had been employed such as; artificial neural network, Bayesian learning, support vector machine, feed-forward back-propagation neural network (FFBPNN), linear discriminant analysis fuzzy -art neural network has been used for classification motor imagery [3].

A person with a mental disability required alternative assistive devices or ways to do motor tasks interlude with the surroundings. BCI had extended antiquity centered based on motor control applications such a helping people with disabilities [3-8] paralyzed body parts [10,9], cursor robotic arms, wheelchairs prosthesis limbs, etc. a lot of application was for the need of disabling community. The hemisphere of the human brain was segmented into four lobes with various functions these lobes were separated by the sulcus. The primary semantic sensory cortex (parietal lobe) and primary motor cortex (temporal lobe) are the very important region in BCI studies. To the present, motor imagery has been helped greatly in the field of medicine to help people by connecting their minds to control devices as well as detecting their brain abnormalities [7]. The main goal of this research is to identify and classify the activities of MI, such as; imaging the movement of the left and right hand, foot, and tongue movement of BCI. Using deep neural network (DNN) and using co-space model (CSP) as extraction feature [7].

Many studies have been conducted to improve the classification of Motor imaginary (MI) data. The efficiency of these systems almost depends on the features selected and the algorithms used for classification purposes. A method for motor imaginary (MI) was proposed for the classification of multichannel electrocorticogram (ECoG), Taking from patients with medically intractable focal epilepsy. Two features extraction method used, which is autoregressive (AR) model coefficients and local binary pattern (LBP) operators. It gives spatial resolution and angular space knowledge along with the gradient boosting (GB) together with ordinary least squares (OLS) algorithm was used as a classifier for improving the efficiency Motor imaginary (MI) classification for ECoG based Brain-Computer Interface (BCI) system. The Results of the Experimental on the BCI Competition III data set I showed that the novel method had very good performance and gave a cross-validation accuracy of 88.8% and accuracy of 93%, respectively [1]. Proposed the alliance of continuous Wavelet Transform (CWT) together with deep-learning-based transfer learning to solve the problem, he achieved an accuracy of 97.06% [11]. A feed-forward back-propagation neural network (FFBPNN) was proposed based on the motor in order to improve the performance motor imagery classification plan. And the accuracy was 99.8% [12] propound better method for EEG Classification from the feature extraction and classification, he analyzed and compared the effectiveness of the following algorithms; CSP-LDA, SCSP-LDA, CSP-RDA, and SCSP-RDA, The detailed study showed that the classification performance of SCSP-RDA was better than CSP-LDA, SCSP-LDA, CSP-RDA in decipher EEG signal and classification algorithms is better than traditional algorithms, improved by 10.75% [13]. Presented EEG datasets for MI BCI from 52 subjects as well as the result of the psychological and physiological questionnaire, EMG datasets, the position of 3d electrodes, and EEGs for non -task-related states. The data was analyzed based on the percentage of bad trials, even- related desynchronization/ synchronization (ERD/ERS) analysis.73.08% datasets gave reasonable approval information [14]. Joined fNIR and EEG signals for classification 8-class problems, by CNN -an artificial intelligence tool. The classification accuracy on voluntary and imagery-related tasks using the bimodal approach for CNN-based BCI gave an excellent result [15]. A deep learning method upon on Restricted Boltzmann Machine (RBM) was proposed, particularly, Frequency domain representation of EEG signals was obtained through fast Fourier transform (FFT) and wavelet package decomposition (WPD) were got and trained three RBMs. And stacked up the RBMs together with the remaining layer to form a frequent deep belief network (FDBN). Conjugate gradient and backpropagation methods were used to fine-tune the FDBN. Public datasets were used and FDBN improved the performance over other states of -the art method [8]. Using and validating the percentage of bad trials, even related desynchronization/synchronization (ERD/ERS), The EEG datasets for the MI BCI dataset of 52 subjects as well as result of psychological and physiological questionnaire was used, it showed 73.08% of the data was statistically used for analysis. Feature extraction method via time-series prediction based on the ANFIS for the BCI application was proposed, the result showed the potential use of the ANFIS time-series suggestion with MVFFV features in Motor imaginary (MI) classification. The ANFIS times series prediction together with MFFV features got better results in the BCI field [16]. He proposed and refined the time-frequency-spatial approach and applied it to a one-dimensional “cursor control” BCI experiment with online feedback. Via offline analysis of the available data, then evaluated, the efficiency of the present refined way was compared with the original time-frequency-spatial methods. The improved performance in forms of classification accuracy was found for the proposed approach, with a mean accuracy rate of 91.1% for two subjects studied [17]. The activity of EEG rhythms (mu rhythms) was studied, in connection to imaging of foot, tongue, right, and left-hand movement with 60 EEG electrodes in nine able-bodied subjects. The hand mu rhythms desynchronized all the subjects. While progressive hand region mu rhythm was studied during tongue or foot motor imagery in all the subjects. The reactive component frequency was 11.7Hz±0.4, while broad-banded the design chromized components at Centre 10.9Hz±0.9, the narrowband and higher frequency were observed on desynchronized components. The classification between the four-motor imagery based on EEG trial was improved [14]. CEMD and IDMSCNN method was proposed for improved the performance of motor imagery (MI) EEG signals classification. The best EMD algorithm is optimized properly and added two conditions to select effective IMFs. The multi-scale convolutional neural network was used as a feature extractor, the result shows that the algorithms can extract the best effective feature or information [18]. A method was proposed and developed for classifying new motor imagery using the Temporal Convolutional Network (TCN). The wider causal convolution within TCN was included, the temporal information in a parallel way with much higher computational efficiency than the traditional RNNs. Time stacked spatial EEG signal had been employed as the input to the TCN. Based on this, both the spatial distribution information and temporal variation of the brain signal were considered. The TCN method had a solution that obtained state-of-the-art performance on the multi-subject and multi-task motor imagery classification. The classification accuracy was 97.89% on 20 subjects and 5 tasks had been obtained [19]. The convolutional neural networks (CNN) were proposed to classify the motor imaginary MI-EEG signals. Three algorithms were used, data augmentation along with an exclusive transfer learning strategy were used to solve the problem of few trials in motor imagery tasks. The analytical regression measured was applied to the raw data for mitigating the stress of EOG on EEG. Then, the simulation results vividly showed the contribution of the proposed algorithm via testing on BCI competition IV dataset 2b. Applying EOG noise removal and data augmentation methods result in a 0.07 improvement in the kappa coefficient. in addition, this proposed transfer learning method led to a 0.06 improvement in form of the kappa coefficient [20]. The analysis and discriminated the EEG patterns of several force stages for motor imaginary (MI) using MRCPs. During the experiment, nine healthy subjects were used to carried-out the experiment, the hand force motor imagery tasks (30% MVC and 10% MVC). Based on MRCPs, the best good difference between the two levels of mental tasks was the manifestation of motor planning. The mean classification accuracy for features including both MRCP and CSP was 78.3%, which was 8.5% higher than the CSP-based features (p<0.001) and 2% higher than the MRCP-based features. The feasibility of using MRCPs for hand force motor imagery classification was achieved [21]. A consumer-grade brain-computer interface device was proposed which has four channels left with right-hand movement to design an interface to collect a total of closely 600 samples for left- and right-hand motor imagery (MI) from two subjects. Hilbert-Huang Transform was used as feature extraction, then support-vector machine (SVM) and k-nearest neighbors (k-NN) algorithms for learning the features and classification were used. This approach has few abilities to classify left- and right-hand motor imagery EEG signals [22]. A new source separation method was proposed, which used the correct model of the head to un-mix the EEG signals via a different source in terms of their physical locations. It recognized sources located in distinct physical regions of the brain. It’s compared with independent component analysis (ICA), the new source separation method had the best spatial specificity as well as allowing higher classification accuracy of 8.6% [23]. A framework for overcoming EEG uncertainties in real-time multiclass MI BCI was proposed, the multiclass extension of the common spatial pattern (CSP) was used for artifact rejection and joint approximate diagonalization (JAD) was used as feature extraction .an adaptive resonance theory (ART) based neuro-fuzzy classifier named self-regulated supervised Gaussian fuzzy adaptive system art (SRSG-Fas Art) was used for multi-class applications. It showed better multiclass classification accuracy as related to state of art method [24]. the workability of Spiking Neural Network (SNN) models in pattern recognition was tested for classification EEG signal with five tasks i.e. rest, left hand, right hand, foot, and tongue movements in motor imaginary MI, the performance of other traditional classifiers as well as the performance of input features with constant values and input feature were compared power spectral density and wavelet decomposition was used as feature extraction stage, the result showed that with a smaller number of Spiking neurons, simple problems can be solved [25]. Using complementary information to ERD/ERS-based features. It was proposed aiming to improve the performance of motor imaginary-based EEG classification with the few-channel condition, and together support vector learning (ESVL) based approach was used to connect the advantages of the ERD/ERS-based features and the event-related potential-based features in motor imaginary-based EEG classification. ESVL classifier could be used posterior probabilities to get ensemble learning and the ESVL-based motor imagery classification approach had the advantage of the merits of ERD/ERS based feature and event-related potential based feature to improve the experimental performance [26]. SCP was proposed with Deep ELM as a classifier with Good efficiency [27]. Motion –onset VEP DBN using EEG time points as classifier with 3.5 improvement [28]. SSVEP and CNN were proposed using Band power as classifier w99.28% & 94.03% improvement [29]. P300, CNN with EEG time as a classifier with an accuracy of 95.5%[30]. Band power using DBN with Significant improvement[8]. EEG based on CNN was proposed MDA is 82.17%, FBCSP is 84.0% [31]. Band power based on DBN was used with improved accuracy [32]. Band power based on CNN+DBN was proposed with improved of 9% [33]. Workload with Band power and Adaptive DBN was proposed [34]. Motor imagery based on band power using DBN as a classifier with 5.26% improvement [35]. ErrP,P300,MRCP.Motor imagery based on EGE time point using CNN as a classifier with 2% improved [36]. Motor imagery based on EEG Raw data 9% improvement [37]. Motor imagery based on EEG Raw using CNN-VAE as classifier with 3% improved [38].

Three models were used in this research the minimum error of the training and testing was a target as =0.001-0.05, learning rate and maximum epoch are 0.01and 15000 respectively, gradient descent (GD) and gradient descent with momentum and LR (GD LR) functions, have similar design and parameter for processing.

The experimental result of the models have an accuracy of 82% with the GD training method as well as GDMLR, the performance depend on the number of iteration that occurs, as more details shown in Figure 3 and Figure 4.

The deep neural network was used for the classification EEG data left hand and right hand of Motor imaginary (MI), with the following range-band of frequency 8-30Hz, 8-14Hz, and 14-30Hz, mu rhythms are best for feature extraction frequency range of (8-14Hz) and improve the performance with the beta band (14-30Hz), lastly, using confusion matrix models for knowing the performance of classification, better classification can be achieved with mu band. The Specificity and sensitivity of the test confusion matrix were found to be 81.4% and 82.0 % respectively shown in figure 9 and 10, and for the train confusion matrix the specificity and sensitivity are also found to be 85.7% and 87.1%, misclassification rates were found to be 17.9% and 13.6%.

Our result showed that GDMALR training has a better performance and is used for classification MI than normal GD. Large data required the higher the network parameter to increase the speed of processing, large data affect the system efficiency. The number of iteration depends on by desired pattern recognition accuracy. Deep neural networks use the above information for the classification of Motor imaginary (MI). Positive regression is obtained as shown in fig.7. With intercept of o.1 and training rate of 0.91193 which show less variation, The classification curve and number of iteration, the number of iteration is much higher than the number of trials, for 20000 iterations, the time taken was 19s which is equal to 0.95s for each iteration, which showed that the method is fast, the best training performance is at 0.042066 at epoch 19861 shown in Fig. 5, the best performance is at 0.0015998 at epoch 23 which is the best controlling flow of data and avoiding overfitting and minimizing the loss. From the Fig. 6. The validation error was zero at 20000 iterations, this means the model used is working perfectly for classification Motor imaginary (MI).

In this work, an approach to classify motor imagery (MI) EEG signals using a deep neural network (DNN) with CSP feature extraction with standard gradient descent (GD) method and gradient descent method with momentum and adaptive LR (GDMLR) has been proposed. the classification accuracy was found to be 87% which is an 11% improvement.

In future work, we plan to use more recent feature extraction and classification methods such as spiking neural network, joint time frequency-space classification and improve this technique to develop a model based on BCI classification tasks.

2 06 149-157 4 2014 10.1007/s13534-014-0128-0 Xu Fangzhou Zhou Weidong Zhen Yilin Yuan Qi Biomedical Engineering Letters Classification of motor imagery tasks for electrocorticogram based brain-computer interface 31 2 01 R1-R13 4 2007 10.1088/1741-2560/4/2/r01 Lotte F Congedo M Lécuyer A Lamarche F Arnaldi B Journal of Neural Engineering A review of classification algorithms for EEG-based brain–computer interfaces 10 190-200 502 2019 10.1016/j.ins.2019.06.008 Lee Seung-Bo Kim Hyun-Ji Kim Hakseung Jeong Ji-Hoon Lee Seong-Whan Kim Dong-Joo Information Sciences Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society 07 2020 10.1109/embc44109.2020.9176612 Kato Masaki Kanoga Suguru Hoshino Takayuki Fukami Tadanori 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Motor Imagery Classification of Finger Motions Using Multiclass CSP 13 3 07 973-983 43 2020 10.1007/s13246-020-00894-z Llorella Fabio R. Patow Gustavo Azorín José M. Physical and Engineering Sciences in Medicine Convolutional neural networks and genetic algorithm for visual imagery classification 07 101991 60 2020 10.1016/j.bspc.2020.101991 Dong Enzeng Zhou Kairui Tong Jigang Du Shengzhi Biomedical Signal Processing and Control A novel hybrid kernel function relevance vector machine for multi-task motor imagery EEG classification 15 4 08 65-72 2 2005 10.1088/1741-2560/2/4/001 Qin Lei He Bin Journal of Neural Engineering A wavelet-based time–frequency analysis approach for classification of motor imagery for brain–computer interface applications 6 06 566-576 25 2017 10.1109/tnsre.2016.2601240 Lu Na Li Tengfei Ren Xiaodong Miao Hongyu IEEE Transactions on Neural Systems and Rehabilitation Engineering A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines 2 06 295-302 189 2010 10.1016/j.jneumeth.2010.03.030 Hsu Wei-Yen Journal of Neuroscience Methods EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features 05 32-39 60 2015 10.1016/j.compbiomed.2015.02.010 Lu Na Li Tengfei Pan Jinjin Ren Xiaodong Feng Zuren Miao Hongyu Computers in Biology and Medicine Structure constrained semi-nonnegative matrix factorization for EEG-based motor imagery classification 11 108886 345 2020 10.1016/j.jneumeth.2020.108886 Kant Piyush Laskar Shahedul Haque Hazarika Jupitara Mahamune Rupesh Journal of Neuroscience Methods CWT Based Transfer Learning for Motor Imagery Classification for Brain computer Interfaces 4 12 2871-2878 9 2018 10.1016/j.asej.2017.12.003 Jana Gopal Chandra Swetapadma Aleena Pattnaik Prasant Kumar Ain Shams Engineering Journal Enhancing the performance of motor imagery classification to design a robust brain computer interface using feed forward back-propagation neural network 09 108833 343 2020 10.1016/j.jneumeth.2020.108833 Fu Rongrong Han Mengmeng Tian Yongsheng Shi Peiming Journal of Neuroscience Methods Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis 1 05 153-159 31 2006 10.1016/j.neuroimage.2005.12.003 Pfurtscheller G. Brunner C. Schlögl A. Lopes da Silva F.H. NeuroImage Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks 12 1 10 7 2019 10.1007/s13755-019-0081-5 Rahman Md. Asadur Uddin Mohammad Shorif Ahmad Mohiuddin Health Information Science and Systems Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network 04 7 05 6 2017 10.1093/gigascience/gix034 Cho Hohyun Ahn Minkyu Ahn Sangtae Kwon Moonyoung Jun Sung Chan GigaScience EEG datasets for motor imagery brain–computer interface 2 06 250-254 14 2006 10.1109/tnsre.2006.875567 Yamawaki N. Wilke C. Liu Z. He B. IEEE Transactions on Neural Systems and Rehabilitation Engineering An Enhanced Time-Frequency-Spatial Approach for Motor Imagery Classification 07 113285 149 2020 10.1016/j.eswa.2020.113285 Tang Xianlun Li Wei Li Xingchen Ma Weichang Dang Xiaoyuan Expert Systems with Applications Motor imagery EEG recognition based on conditional optimization empirical mode decomposition and multi-scale convolutional neural network 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) 10 2019 10.1109/bibe.2019.00148 Lu Na Yin Tao Jing Xue 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) A Temporal Convolution Network Solution for EEG Motor Imagery Classification 2019 27th Iranian Conference on Electrical Engineering (ICEE) 04 2019 10.1109/iraniancee.2019.8786636 Parvan Milad Ghiasi Amir Rikhtehgar Rezaii Tohid Yousefi Farzamnia Ali 2019 27th Iranian Conference on Electrical Engineering (ICEE) Transfer Learning based Motor Imagery Classification using Convolutional Neural Networks 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 07 2018 10.1109/embc.2018.8512184 Wang Kun Xu Minpeng Zhang Shanshan Ke Yufeng Ming Dong 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Analysis and Classification for EEG Patterns of Force Motor Imagery Using Movement Related Cortical Potentials 2019 IEEE International Conference on Cognitive Computing (ICCC) 07 2019 10.1109/iccc.2019.00023 Wang Ganyu Martin Miguel Hung Patrick MacDonald Shane 2019 IEEE International Conference on Cognitive Computing (ICCC) Towards Classifying Motor Imagery Using a Consumer-Grade Brain-Computer Interface 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society 07 2020 10.1109/embc44109.2020.9176690 Daly Ian Rybar Milan 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Neural component analysis: source localisation for motor imagery classification 03 101749 57 2020 10.1016/j.bspc.2019.101749 Jafarifarmand Aysa Badamchizadeh Mohammad Ali Biomedical Signal Processing and Control Real-time multiclass motor imagery brain-computer interface by modified common spatial patterns and adaptive neuro-fuzzy classifier 02 130-143 122 2020 10.1016/j.neunet.2019.09.037 Virgilio G. Carlos D. Sossa A. Juan H. Antelis Javier M. Falcón Luis E. Neural Networks Spiking Neural Networks applied to the classification of motor tasks in EEG signals 09 105464 193 2020 10.1016/j.cmpb.2020.105464 Luo Jing Gao Xing Zhu Xiaobei Wang Bin Lu Na Wang Jie Computer Methods and Programs in Biomedicine Motor imagery EEG classification based on ensemble support vector learning 1-11 2015 2015 10.1155/2015/129021 Ding Shifei Zhang Nan Xu Xinzheng Guo Lili Zhang Jian Mathematical Problems in Engineering Deep Extreme Learning Machine and Its Application in EEG Classification 01 80-92 275 2017 10.1016/j.jneumeth.2016.11.002 Ma Teng Li Hui Yang Hao Lv Xulin Li Peiyang Liu Tiejun Yao Dezhong Xu Peng Journal of Neuroscience Methods The extraction of motion-onset VEP BCI features based on deep learning and compressed sensing 22 2 02 e0172578 12 2017 10.1371/journal.pone.0172578 Kwak No-Sang Müller Klaus-Robert Lee Seong-Whan Schwenker Friedhelm PLOS ONE A convolutional neural network for steady state visual evoked potential classification under ambulatory environment 3 03 433-445 33 2011 10.1109/tpami.2010.125 Cecotti H Graser A IEEE Transactions on Pattern Analysis and Machine Intelligence Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces 07 11 08 5391-5420 38 2017 10.1002/hbm.23730 Schirrmeister Robin Tibor Springenberg Jost Tobias Fiederer Lukas Dominique Josef Glasstetter Martin Eggensperger Katharina Tangermann Michael Hutter Frank Burgard Wolfram Ball Tonio Human Brain Mapping Deep learning with convolutional neural networks for EEG decoding and visualization 12 141-145 274 2016 10.1016/j.jneumeth.2016.10.008 Sturm Irene Lapuschkin Sebastian Samek Wojciech Müller Klaus-Robert Journal of Neuroscience Methods Interpretable deep neural networks for single-trial EEG classification 30 1 11 016003 14 2016 10.1088/1741-2560/14/1/016003 Tabar Yousef Rezaei Halici Ugur Journal of Neural Engineering A novel deep learning approach for classification of EEG motor imagery signals 03 30-47 33 2017 10.1016/j.bspc.2016.11.013 Yin Zhong Zhang Jianhua Biomedical Signal Processing and Control Cross-session classification of mental workload levels using EEG and an adaptive deep learning model 03 93-110 140 2017 10.1016/j.cmpb.2016.12.005 Yin Zhong Zhao Mengyuan Wang Yongxiong Yang Jingdong Zhang Jianhua Computer Methods and Programs in Biomedicine Recognition of emotions using multimodal physiological signals and an ensemble deep learning model 1-1 2020 10.1109/tcds.2020.3007453 Wu Dongrui Xu Yifan Lu Bao-Liang IEEE Transactions on Cognitive and Developmental Systems Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016 10th International Conference, ICIC 2014 Taiyuan, China August 2014 10.1007/978-3-319-09330-7_25 An Xiu Kuang Deping Guo Xiaojiao Zhao Yilu He Lianghua Intelligent Computing in Bioinformatics A Deep Learning Method for Classification of EEG Data Based on Motor Imagery 29 3 01 551 19 2019 10.3390/s19030551 Dai Mengxi Zheng Dezhi Na Rui Wang Shuai Zhang Shuailei Sensors EEG Classification of Motor Imagery Using a Novel Deep Learning Framework 11 7 04 1736 19 2019 10.3390/s19071736 Majidov Ikhtiyor Whangbo Taegkeun Sensors Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods 1 Berkeley, CA Apress 2017 978-1-4842-2844-9 10.1007/978-1-4842-2845-6 Kim Phil MATLAB Deep Learning-With Machine Learning, Neural Networks and Artificial Intelligence 2018 10.31227/osf.io/8erjd Wanto Anjar Andani Sundari Retno Poningsih Poningsih Dewi Rafiqa Lubis Muhammad Ridwan Saputra Widodo Kirana Ika Okta INA-Rxiv Analysis of Standard Gradient Descent with GD Momentum and Adaptive LR for SPR Prediction 4 06 439-454 10 1986 10.1016/0165-1684(86)90050-2 Dimolitsas Spiros Lister Paul F Signal Processing Waveform preserving encoders for the compression of EEG data