000			02766nam a22001577a 4500
082			_a610
100			_aSultan, Hajrah _9120894
245			_aClassification of Reach and Grasp Motions from EEG Signals using Deep Convolutional Neural Networks (CNN) / _cHajrah Sultan
264			_aIslamabad : _bSMME- NUST; _c2022.
300			_a73p. _bSoft Copy _c30cm
500			_aClassification of neural correlates of hand motions from EEG signals recently gained attention of researchers for the development of BCI systems for persons suffered from stroke, spinal cord injury who are not able to do voluntary movements or person with amputated arm or legs. Commonly LDA, SVM and K-NN models are used for the classification of hand motions, CNN and hybrid models are also used but most methods include the complex methods or preprocessing of EEG data and extraction of time or frequency domain features from the preprocessed signals which is a time consuming and lack flexibility because the EEG signals vary from human to human. In this thesis a Deep CNN model for end-to-end0learning of neural corelates for reach and grasp actions is introduced, aiming to increase rate of recognition & balanced classification0accuracy throughout all the subjects. A new model of CNN for movement classification is proposed that can also be used on the edge devices because of its smaller size for the development of BCI systems. In the proposed model separable convolutional blocks are used which reduce the number of parameters and hence the size of model also decreases. The dataset that is used for the testing of model is BNCI Horizon 2020 Reach and Grasp action dataset that is publicly available dataset. The dataset is also tested on 3 machine learning models LDA, SVM and K-NN are used, in which input is given in the form of time domain feature set the average accuracies achieved on these models are 60.77 (±3.80 STD), 66.73 (±2.86 STD), and 79.81 (±3.11 STD) respectively on the unseen dataset. Then the dataset is tasted on proposed Deep learning model along with DeepConvNet and EffNet models. The proposed model achieves the average classification accuracy of 92.44 (±4.13 STD), 92.9 (±4.23 STD) and 81.7(±5.68 STD). The model proposed achieves the same accuracy as DeepConvNet, but the size of proposed model is far smaller than the DeepConvNet model. Results shows that the proposed model shows the improved results with less variation of results within the subjects. Which will become helpful in the creation of real time BCI systems.
650			_aMS Biomedical Engineering (BME) _9119509
700			_aSupervisor : Dr. Muhammad Asim Waris _9119524
856			_u http://10.250.8.41:8080/xmlui/handle/123456789/31812
942			_2ddc _cTHE
999			_c607925 _d607925