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Engagement is described as a state in which an individual involved in an activity can ignore other influences. The engagement level is important to obtaining good performance especially under study conditions. Numerous methods using electroencephalograph (EEG), electrocardiograph (ECG), and near-infrared spectroscopy (NIRS) for the recognition of engagement have been proposed. However, the results were either unsatisfactory or required many channels. In this study, we introduce the implementation of a low-density hybrid system for engagement recognition. We used a two-electrode wireless EEG, a wireless ECG, and two wireless channels NIRS to measure engagement recognition during cognitive tasks. We used electrooculograms (EOG) and eye tracking to record eye movements for data labeling. We calculated the recognition accuracy using the combination of correlation-based feature selection and k-nearest neighbor algorithm. Following that, we did a comparative study against a stand-alone system. The results show that the hybrid system had an acceptable accuracy for practical use (71.65 ± 0.16%). In comparison, the accuracy of a pure EEG system was (65.73 ± 0.17%), pure ECG (67.44 ± 0.19%), and pure NIRS (66.83 ± 0.17%). Overall, our results demonstrate that the proposed method can be used to improve performance in engagement recognition., Competing Interests: The authors declare no conflict of interest.

In this study, a monitoring system of cognitive state in usual behavior without restraint using wireless EEG, ECG and NIRS on developmental disorder children such as mental retardation was developed. By using this system we would like to investigate the education training effect on cognitive state in a mental retardation child in four years. The aim of special education is to make adaptations, accommodations and modification that allow a child with a mental retardation to succeed in classroom. This education training separated into two systems, resting state and studying state. After the measurement, we calculated the EEG power spectrum of alpha, theta bands and the low frequency power (LF), high frequency power (HF), and LH/HF value from the RR interval in ECG. At the same time we calculated the changes in concentration of oxyhemoglobin ([oxy-Hb]). The result in studying state showed theta power is lower than beta power in study state in the other hand beta power is lower than theta power in resting state From LF/HF activity we could know that sympathetic activity is increasing and from the result that has obtained from ECG, the parasympathetic activity is decreasing the time. NIRS showed the increasing in study states and decreasing in resting states at Fp2 area from oxyhemoglobin analysis.