A review of hybrid brain-computer interface systems <s> EEG-EOG Hybrid BCI. <s> 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. <s> BIB001 </s> A review of hybrid brain-computer interface systems <s> EEG-EOG Hybrid BCI. <s> Nowadays, everybody knows what a hybrid car is. A hybrid car normally has 2 engines, its main purpose being to enhance energy efficiency and reduce CO2 output. Similarly, a typical hybrid brain-computer interface (BCI) is also composed of 2 BCIs or at least one BCI and another system. Such a hybrid BCI, like any BCI, must fulfil the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces some hybrid BCIs which have already been published or are currently in development or validation, and some concepts for future work. The BCIs described classify 2 EEG patterns: One is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). The hybrid BCI can either have more than one input whereby the inputs are typically processed simultaneously or operate 2 systems sequentially, whereby the first system can act as a “brain switch”. In the case of self-paced operation of a SSVEP-based hand orthosis control with an motor imagery-based switch it was possible to reduce the rate of false positives during resting periods by about 50% compared to the SSVEP BCI alone. It is shown that such a brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Another interesting approach is a hybrid BCI with simultaneous operations of ERD- and SSVEP-based BCIs. Here it is important to prove the existing promising offline simulation results with online experiments. Hybrid BCIs can also use one brain signal and another input. Such an additional input can be a physiological signal like the heart rate but also a signal from an external device like, an eye gaze control system. <s> BIB002 </s> A review of hybrid brain-computer interface systems <s> EEG-EOG Hybrid BCI. <s> A hybrid brain–computer interface (BCI) system that combines a self-paced BCI and an eye-tracker is proposed for text-entry applications. To make a text-entry of a letter/word, the user must gaze at the target for at least a specific period of time (called the dwell time) and then activate the self-paced BCI with an attempted hand extension. Although the self-paced BCI is available for use at any time, a built-in sleep mode is activated when the user is not looking at a letter/word or when the user gazes at a letter/word for less than the dwell time. Such a design has the advantage of greatly minimizing the false positive outcomes compared to the state-of-art self-paced BCIs. To further improve the system's performance, a method that adaptively updates the BCI classifier is also proposed. The results from seven able-bodied individuals show great improvements compared to the pure self-paced BCI. For dwell times of 0.75 and 1.00 s, the number of false-positives/minute is significantly reduced to 2.5 and 1.7, at acceptable average true positive rates of 54.5% and 54.1%, respectively. <s> BIB003 </s> A review of hybrid brain-computer interface systems <s> EEG-EOG Hybrid BCI. <s> BackgroundA novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard. To select a letter, the user must gaze at the target for at least a specific period of time (dwell time) and then activate the BCI by performing a mental task. Unfortunately, electroencephalogram (EEG) signals are often contaminated with artefacts. Artefacts change the quality of EEG signals and subsequently degrade the BCI’s performance.MethodsTo remove artefacts in EEG signals, the proposed algorithm uses the stationary wavelet transform combined with a new adaptive thresholding mechanism. To evaluate the performance of the proposed algorithm and other artefact handling/removal methods, semi-simulated EEG signals (i.e., real EEG signals mixed with simulated artefacts) and real EEG signals obtained from seven participants are used. For real EEG signals, the hybrid BCI system’s performance is evaluated in an online-like manner, i.e., using the continuous data from the last session as in a real-time environment.ResultsWith semi-simulated EEG signals, we show that the proposed algorithm achieves lower signal distortion in both time and frequency domains. With real EEG signals, we demonstrate that for dwell time of 0.0s, the number of false-positives/minute is 2 and the true positive rate (TPR) achieved by the proposed algorithm is 44.7%, which is more than 15.0% higher compared to other state-of-the-art artefact handling methods. As dwell time increases to 1.0s, the TPR increases to 73.1%.ConclusionsThe proposed artefact removal algorithm greatly improves the BCI’s performance. It also has the following advantages: a) it does not require additional electrooculogram/electromyogram channels, long data segments or a large number of EEG channels, b) it allows real-time processing, and c) it reduces signal distortion. <s> BIB004
Since the majority of people with disabilities can have control on their eye movement, the electrooculogram (EOG) signals could be an appropriate option as input signals for BCI system. EEG and EOG combination was introduced to make a hybrid BCI BIB001 . In this study, EOG and EEG signals were taken from two channels and were utilized simultaneously. The technique in generating control commands based on EEG/EOG hybrid BCI is explained as follows. The "turn left" and "turn right" control commands were derived from EOG signals based on the right/left eye gazing pattern. Subjects performed maximum right and left eye gazing, and the positive and negative potential were recorded, respectively. 75% of the recorded amplitudes were considered as the threshold for detecting the right and left eye movements. If the amplitude recorded from the right eye during the trials was greater than the threshold related to the right eye, the "turn right" command was detected. For the "turn left" control command detection, the absolute value of the negative potential recorded from the left eye should be greater than the related threshold. If both values were less than the related threshold values, the "no action" control command was detected. Classification accuracy of 100% was achieved for "turn left" and "turn right" control commands. Average accuracy of 95% was achieved for "no action" control command. The "forward", "no action", and "completely stop" control commands were detected from EEG. The parameter used for deriving the control commands from the EEG was PSD in the alpha and beta band. A threshold was considered for comparison to the maximum PSD detected from the alpha and beta band. Three subjects in three trials and 50 control commands in each trial performed experiments. At the beginning of the test, software calibration was performed by the subjects. The maximum PSD in alpha band was recorded from the subjects with closed eyes and 75% of the PSD from the calibration was considered as threshold. For the "completely stop" command, the subjects were instructed to close their eyes to increase the alpha activity. Then, the maximum PSD in the alpha band was compared to a threshold. If the maximum PSD was greater than the threshold, the "completely stop" command was issued. For the "forward" control command, the subjects were instructed to think about moving forward. If the maximum PSD recorded in the beta band was greater than the maximum PSD recorded in the alpha band, the "forward" control was issued. The "no action" control command was issued if the maximum PSD in the beta band was less than the maximum PSD in the alpha band and both were less than the threshold. The average classification accuracy over the whole trials was 100% for "completely stop" and 87% for "forward" control commands. The "no action" control command was common in both EEG and EOG control command detection parts and the average classification accuracy of 95% was related to the both parts of the task. The interface implementation and the feedback were presented by employing the test on a toy truck. In addition to high classification accuracy, small number of electrodes and short training time showed the advantage of introduced hybrid. In another approach BIB003 , a self-paced BCI system was combined with an eye-tracker system to establish a selfpaced hybrid BCI BIB002 . In this system, for cursor control, an eye tracker was utilized by detecting the user's eye gaze. A BCI was utilized for clicking on a selected item on computer screen. Subjects were instructed to first gaze at an intended letter on the screen to select it, then for click on the selected letter, hand extension movement was needed. EEG was recorded from the cortex area with 15 electrodes. For EOG, two pairs of electrodes were used. In addition, four pairs of electrodes were used for recording facial muscle activities, from which the facial muscle artefacts can be detected. PSD of 30 combinations of bipolar EEG channels was computed based on Fast Fourier Transform (FFT). For feature selection, stepwise LDA was considered  . Then, the features were classified with LDA and adaptive LDA and for more improvement moving average. For removing EOG and EMG artefacts from the EEG signal, an algorithm was proposed in BIB004 , which showed improvement in the performance of the introduced selfpaced hybrid BCI BIB003 . Stationary wavelet transform and an adaptive threshold mechanism were used in the proposed algorithm. Results were evaluated based on two types of data; real EEG signals with simulated artefacts (semi-simulated EEG signals) and real EEG signals. In semi-simulated EEG signals, signal distortion was decreased and in real EEG signals, the true positive rate was increased using the proposed algorithm.