Drowsiness detection has been studied in the context of evaluating products, assessing driver alertness, and managing office environments. Drowsiness level can be readily detected through measurement of human brain activity. The electroencephalogram (EEG), a device whose application relies on adhering electrodes to the scalp, is the primary method used to monitor brain activity. The many electrodes and wires required to perform an EEG place considerable constraints on the movement of users, and the cost of the device limits its availability. For these reasons, conventional EEG devices are not used in practical studies and businesses. Many potential practical applications could benefit from the development of a wire-free, low-priced device; however, it remains to be elucidated whether portable EEG devices can be used to estimate human drowsiness levels and applied within practical research settings and businesses. In this study, we outline the development of a drowsiness detection system that makes use of a low-priced, prefrontal single-channel EEG device and evaluate its performance in an offline analysis and a practical experiment. Firstly, for the development of the system, we compared three feature extraction methods: power spectral density (PSD), autoregressive (AR) modeling, and multiscale entropy (MSE) for detecting characteristics of an EEG. In order to efficiently select a meaningful PSD, we utilized step-wise linear discriminant analysis (SWLDA). Time-averaging and robust-scaling were used to fit the data for pattern recognition. Pattern recognition was performed by a support vector machine (SVM) with a radial basis function (RBF) kernel. The optimal hyperparameters for the SVM were selected by the grind search method so as to increase drowsiness detection accuracy. To evaluate the performance of the detections, we calculated classification accuracy using the SVM through 10-fold cross-validation. Our model achieved a classification accuracy of 72.7% using the PSD with SWLDA and the SVM. Secondly, we conducted a practical study using the system and evaluated its performance in a practical situation. There was a significant difference (* p < 0.05) between the drowsiness-evoked task and concentration-needed task. Our results demonstrate the efficacy of our low-priced portable drowsiness detection system in quantifying drowsy states. We anticipate that our system will be useful to practical studies with aims as diverse as measurement of classroom mental engagement, evaluation of movies, and office environment evaluation.

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.

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A brain-computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets-AMUSE and PASS2Dusing the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs.

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