Abstract
In this paper, we propose an automated computer platform for the purpose of classifying Electroencephalography (EEG) signals associated with left and right hand movements using a hybrid system that uses advanced feature extraction techniques and machine learning algorithms. It is known that EEG represents the brain activity by the electrical voltage fluctuations along the scalp, and Brain-Computer Interface (BCI) is a device that enables the use of the brain neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements. In our research work, we aspired to find the best feature extraction method that enables the differentiation between left and right executed fist movements through various classification algorithms. The EEG dataset used in this research was created and contributed to PhysioNet by the developers of the BCI2000 instrumentation system. Data was preprocessed using the EEGLAB MATLAB toolbox and artifacts removal was done using AAR. Data was epoched on the basis of Event-Related (De) Synchronization (ERD/ERS) and movement-related cortical potentials (MRCP) features. Mu/beta rhythms were isolated for the ERD/ERS analysis and delta rhythms were isolated for the MRCP analysis. The Independent Component Analysis (ICA) spatial filter was applied on related channels for noise reduction and isolation of both artifactually and neutrally generated EEG sources. The final feature vector included the ERD, ERS, and MRCP features in addition to the mean, power and energy of the activations of the resulting independent components of the epoched feature datasets. The datasets were inputted into two machine-learning algorithms: Neural Networks (NNs) and Support Vector Machines (SVMs). Intensive experiments were carried out and optimum classification performances of 89.8 and 97.1 were obtained using NN and SVM, respectively.
Highlights
The importance of understanding brain waves is increasing with the ongoing growth in the Brain-Computer Interface (BCI) field, and as computerized systems are becoming one of the main tools for making people’s lives easier, BCI or BrainMachine Interface (BMI) has become an attractive field of research and applications, BCI is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements [1,2,3,4].The term “Electroencephalography” (EEG) is the process of measuring the brain’s neural activity as electrical voltage fluctuations along the scalp that results from the current flows in brain’s neurons [5]
After the EEG datasets were analyzed as described in the previous section, the activation vectors were calculated for each of the resulted epochs’ datasets as the multiplication of the Independent Component Analysis (ICA) weights and ICA sphere for each dataset subtracting the mean of the raw data from the multiplication results
The constructed features were represented in a numerical format that is suitable for use with machine learning algorithms [33, 34]
Summary
The importance of understanding brain waves is increasing with the ongoing growth in the Brain-Computer Interface (BCI) field, and as computerized systems are becoming one of the main tools for making people’s lives easier, BCI or BrainMachine Interface (BMI) has become an attractive field of research and applications, BCI is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements [1,2,3,4].The term “Electroencephalography” (EEG) is the process of measuring the brain’s neural activity as electrical voltage fluctuations along the scalp that results from the current flows in brain’s neurons [5]. The importance of understanding brain waves is increasing with the ongoing growth in the Brain-Computer Interface (BCI) field, and as computerized systems are becoming one of the main tools for making people’s lives easier, BCI or BrainMachine Interface (BMI) has become an attractive field of research and applications, BCI is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements [1,2,3,4]. BCI measures EEG signals associated with the user’s activity applies different signal processing algorithms for the purpose of translating the recorded signals into control commands for different applications [7]. BCI is a highly interdisciplinary research topic that combines medicine, neurology, psychology, rehabilitation engineering, HumanComputer Interaction (HCI), signal processing and machine learning [10]
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