EEG-Based Condition Clustering using Self-Organising Neural Network Map

Electroencephalography (EEG) has recently emerged as a useful neurophysiological biomarker for characterizing different physiological and pathological conditions of healthy and un-healthy brain activity measurements. However, the complexity and high temporal resolution of the EEG signal data has brought about the need for efficient and accurate automated methods for distinguishing mental tasks activities and the recording conditions. Distinguishing mental tasks with high accuracy is pertinent for early detection and clinical diagnostic of several neurodegenerative diseases. Expert clinicians are needed in order to distinguish between mental tasks and EEG recording conditions, which is a manual process that is prone to inefficiencies and errors especially when the EEG data is miss-annotated at the recording stage. This paper proposes the application of a Self-organizing neural network Map (SOM) with Learning Vector Quantization (LVQ) for EEG Eyes Open (EO) and Eyes Closed (EC) condition classification. This was achieved with classification accuracy of 88.5 %. The proposed approach shows good performance and hence the method can be readily applied to other classification/clustering problems on brain measurements in the Brain Computer Interface (BCI) arena.