Improvement of human error prediction accuracy in single-trial analysis of electroencephalogram.

Abstract

The prevention of human error is an important task that has already been researched. Previous studies have shown that EEG signals can predict the occurrence of human errors. However, high accuracy has not yet been achieved in a single-trial analysis. This study is aimed to improve the accuracy of single-trial analysis, and propose a method for anomaly detection with auto encoder(AE). In the experiment, we conducted "Press the button(Go)" or "Do nothing(No-Go)" according to the visual stimulus and analyzed the EEG signal from -1000 ms to 0 ms when the stimulus was displayed. We prepared two types of inputs, time series data and frequency spectrum, and an AE was trained to reconstruct the inputs. We then calculated the difference between the reconstructed data and input data and predicted human error by its largeness. In the prediction using Support Vector Machine (SVM) based on the frequency spectrum, some over-fitting occurred and the average accuracy was 43 %. In the prediction using anomaly detection with frequency spectrum was 53 % and could not be classified. The time series data was 63 % which improved the accuracy. A previous study has shown frequency-dependent features such as -band activity and rhythm, as precursors of human error. However, in single-trial analysis, we obtained a higher accuracy by time series data than when by using the frequency spectrum. However, there was no noticeable difference between SVM and anomaly detection methods other than over-fitting. Therefore, in this case, the improvement in accuracy by the anomaly detection method could not be confirmed. However, the result suggests that it is more effective to use the frequency spectrum than the time series data in the single-trial analysis in the future.