EEG Signal Classification Using Support Vector Machines (SVM)

Research and application of electroencephalogram (EEG) signal classification using Support Vector Machines (SVM)

In this study, we employ Support Vector Machines (SVM) to classify electroencephalogram (EEG) signals. EEG is a non-invasive technique for recording brain activity that provides crucial information about human brain function and abnormalities. By classifying EEG signals, we can identify different EEG patterns and apply them to medical diagnosis, brain-computer interfaces, and other related fields.

Prior to EEG signal classification, we need to perform preprocessing steps including filtering, noise removal, and feature extraction. In code implementation, this typically involves using bandpass filters (e.g., 0.5-40 Hz) to eliminate artifacts, applying wavelet transform for noise reduction, and extracting time-domain features (mean, variance) or frequency-domain features (power spectral density) using functions like scipy.signal or MATLAB's Signal Processing Toolbox. Subsequently, we utilize the SVM algorithm to train a classification model and make predictions on new EEG signals. The SVM implementation can leverage libraries such as scikit-learn's SVC class with appropriate kernel selection (linear, RBF, or polynomial) and parameter optimization through grid search.

Through this research, we aim to enhance the accuracy and stability of EEG signal classification, providing robust support for further studies in EEG signal analysis and application domains. The classification pipeline typically involves data standardization using StandardScaler, model validation through k-fold cross-validation, and performance evaluation metrics such as accuracy, precision, and recall scores.