Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram (EEG) is employed to control the forword, no action, and complete stop motions of the machine. By using only 2-channel biosignals, the average classification accuracy of more than 95% can be achieved.

Steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) system is one of the most accurate assistive technologies for the persons with severe disabilities. However, the existing visual stimulation patterns still lead to the eyes fatigue. Therefore, in this paper, we propose a novel visual stimulator using the idea of the motion visual stimulus to reduce the eyes fatigue while maintaining the merit of the SSVEP phenomena. Two corresponding feature extractions, i.e. 1) attention detection and 2) SSVEP detection, are also proposed to capture the phenomena of the proposed motion visual stimulus. Two-class classification accuracy of both features is approximately 80%, where the maximum accuracy using the attention detection is 90%, and the maximum accuracy using the SSVEP detection is 100%.

This paper proposes the hybrid BCI modalities for wheelchair control by taking into account weakness of the current BCI systems. The idea is to combine two hybrid BCI systems with the intelligent wheelchair for three states, i.e. normal, fatigue, and emergency states. First system is the hybrid steady state visual evoked potential (SSVEP) and alpha rhythm BCI which is designed to use in the normal state. Second system is the hybrid motion visual stimulus and alpha rhythm which can be employed during the fatigue state (after using the first system). For the experiment, subjects are asked to perform SSVEP system for 30 minutes (until the fatigue states occur). Then, the subjects will be asked to perform the hybrid motion visual stimulus and alpha rhythm testing. The accuracy of the proposed system during fatigue state is approximately 85.62%. With this idea, BCI controlled wheelchair can be efficiently employed in reality.

This paper proposes a half-field steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system to enhance the number of limited commands obtained from the existing SSVEP-based BCI methods. With the theory of vision perception and the concept of the existing half-field SSVEP-based BCI system, we propose the new stimulation pattern that, by using only one frequency, four commands can be generated with the average classification accuracy of approximately 77%. By using only one frequency, eye fatigue can be reduced. Furthermore, this method can be efficiently used to further increase the number of commands for the existing SSVEP-based BCI system.

This paper proposes a steady‐state auditory stimulus modality and a detection algorithm to replace steady‐state visual evoked potential (SSVEP)‐based brain–computer interface (BCI) systems during visual fatigue periods. The optimal speaker position for the steady‐state auditory evoked potential (SSAEP)‐based BCI system and possible electrode positions are investigated. Using the proposed system, an accuracy of 85% for two commands was achieved based on the T3–T5 and T4–T6 electrode positions using only one speaker. SSAEP is a promising BCI modality for mitigating the problem of eye fatigue that often occurs during the use of SSVEP‐based BCI systems. However, SSAEP‐based BCI systems suffer from low accuracy. To increase accuracy, we propose a new enhanced SSAEP training method. The training process was enhanced by instructing users to control their attention levels while simultaneously detecting an auditory stimulus frequency. Furthermore, we propose a corresponding single‐frequency, multi‐command BCI paradigm for the proposed training method. With the proposed paradigm, four commands can be detected using only one auditory stimulus frequency. The proposed training system yielded an accuracy of ∼81% compared to 66% for sessions performed without the proposed training method. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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To solve the eye fatigue problem on using the well known steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system, the steady state auditory evoked potential (SSAEP) becomes one of the promising BCI modalities. However, SSAEP-based BCI system still suffers from the low accuracy. To increase the accuracy, in this paper, we propose the new training method to enhance the SSAEP training session. The training process is enhanced by making the users control their attention levels simultaneously with the detected auditory stimulus frequency. Furthermore, with the proposed training method, we also propose the corresponding single-frequency/multi-commands BCI paradigm. With the proposed paradigm, four commands can be detected by using only one auditory stimulus frequency. The proposed training system yields approximately 81% accuracy compared with 66% of the session without performing the proposed training.