EEG-based golf putt outcome prediction using support vector machine

In this paper, a method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) signals recorded before the impact between the putter and the ball. This method can be used into a brain-computer interface system that encourages golfers for putting when their EEG patterns show that they are ready. In the proposed method, multi-channel EEG trials of a golfer are collected from the electrodes placed at different scalp locations in one particular second when she/he concentrates on putting preparation. The EEG trials are used to predict two possible outcomes: successful or failed putts. This binary classification is performed by the support vector machine (SVM). Based on the collected time-domain EEG signals, the spectral coherences from 22-pair electrodes are calculated and then used as the feature and input for the SVM algorithm. Our experimental results show that the proposed method using EEG coherence significantly outperforms the SVM with other popular features such as power spectral density (PSD), average PSD, power, and average spectral coherence.