Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces

Abstract

Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user’s mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.

A Novel Fast ICA-FBCCA Algorithm and Convolutional Neural Network for Single-Flicker SSVEP-Based BCIs

“…Convolutional neural network (CNN) is a widely used deep learning (DL) approach for extracting feature representations in image classification problems [42]- [44], and its capacity for discovering invariant features has shown potential for enhancing methods applied in EEG signal analysis [45]- [48]. Recently, CNN has attracted attention in SSVEP-based EEG [41], [49]- [51] and demonstrated higher performance in comparison with other traditional classifier methods [39]- [41] such as the CCA (when used as the classifier [3], [52]), neural network (NN), k-nearest neighbor (K-NN), linear discriminant analysis (LDA), and support vector machine (SVM) methods. Moreover, CNN has advantages including (i) local connections [53], (ii) extract hierarchical features by sharing the weights [54], and (iii) pooling and the use of many layers [55].…”