We have been developing a cognitive assessment system based on an electroencephalography-based brain-machine interface (EEG-based BMI) that uses event-related potentials (ERPs) as a virtual “EEG switch.” ERP reflects a temporal change in attention and is also known as a potential biomarker for assessing cognitive functions for older people even with motor decline. We recorded EEG data from 26 healthy adult subjects (18-79 years old) who performed the target-selection task with the EEG switch and compared the result with that of a standard neuropsychological test, the “Trail Making Test” (TMT). We found a correlation between them, and the result showed that this system has the potential for early detection of cognitive decline in order people.

This study proposes a methodology for constructing a music database for research purposes. We focused on the feasibility of an efficient and reliable technique to collect music stimuli that induce a variety of emotions. We selected iconic phrases from 4 famous classical pieces with 4 controls of scales, respectively. We modified each piece into 4 categories by changing the properties in terms of “mode” (converting a major piece to a minor, or vice versa) and “tempo” (changing the BPM speed faster or slower). We verified our method by a Music Information Retrieval (MIR) system. The MIR analyses showed that most of the pieces were successfully positioned in the intended categories (major/minor mode at a high/low tempo). The result suggests that this may be an efficient method to construct an objective music database that is independent of the psychological evaluations.

The Trail Making Test (TMT) is a common neuropsychological test that assesses selective attention and executive functions and is used to detect dementia. However, the test lacks efficiency due to its paper-based format. In this study, we digitally transformed (DX) the TMT using Scratch, a high-level block-based visual programming language. This is a user-friendly programming language and allows beginners to create advanced games and programs. In the DX-TMT we developed, two new tasks, one of which with an increased difficulty level, were added in addition to the two original tasks from the paper-based TMT. The results showed that the DX-TMT reproduced the basic features of the paper-based TMT. The additional task with increased difficulty was associated with a longer completion time than the original tasks, especially in elderly subjects, with greater individual differences. These results suggest that the DX-TMT could be utilized for the early detection of mild cognitive impairment.

Our goal is to promote brain health through “bSports” (brain-Sports), a form of social sport competition using brain-machine interfaces. As the core technology, we have been developing an electroencephalography-based cognitive training system, “Neurotrainer,” using event-related potential as a virtual “mind switch” to control games. Here, we aimed to assess the performance of the Neurotrainer under competitive conditions between players. Fifteen pairs of healthy adults, including older people with tendency of degrading motor functions, were involved in a one-hour session of 4 games. Each player activated the mind switch to select one of eight pictograms to control his/her robot avatar for its desired action. All players regardless of age performed well at the level of about 85% success rate and reported that they had fun with our body-free technologies. These results suggest that bSports is an effective cognitive training and social activity that can be appreciated by people across all generations.