We investigated event-related potentials (ERPs) elicited by chord changes using a custom-built electroencephalography-based decoding system. An oddball paradigm was employed to examine how harmonic structures influence auditory deviance detection, extending the previous work on pure tones. To quantify the neural responses, we used a discriminant score based on linear discriminant analysis, offering an objective measure of response strength. Experiments with healthy adults revealed that pitch changes in minor chords, particularly from lower to higher ranges, elicit the strongest ERP responses, suggesting a saliency effect of pitch height. Mode changes (e.g., major to minor) showed greater variability, possibly because of individual differences in perception and musical background. Importantly, compared to single tones, chords engage higher-level auditory processing, owing to their harmonic complexity. This study demonstrates the utility of ERP-based measures for investigating neural responses to structured auditory stimuli and highlights how harmonic features contribute to attentional modulation in music perception.

Background/Objectives: Motor decline in older adults can hinder cognitive assessments. To address this, we developed a brain-computer interface (BCI) using electroencephalography (EEG) and event-related potentials (ERPs) as a motor-independent EEG Switch. ERPs reflect attention-related neural activity and may serve as biomarkers for cognitive function. This study evaluated the feasibility of using ERP-based task success rates as indicators of cognitive abilities. The main goal of this article is the development and baseline evaluation of the Neurodetector system (incorporating the EEG Switch) as a motor-independent tool for cognitive assessment in healthy adults. Methods: We created a system called Neurodetector, which measures cognitive function through the ability to perform tasks using a virtual one-button EEG Switch. EEG data were collected from 40 healthy adults, mainly under 60 years of age, during three cognitive tasks of increasing difficulty. Results: The participants controlled the EEG Switch above chance level across all tasks. Success rates correlated with task difficulty and showed individual differences, suggesting that cognitive ability influences performance. In addition, we compared the pattern-matching method for ERP decoding with the conventional peak-based approaches. The pattern-matching method yielded a consistently higher accuracy and was more sensitive to task complexity and individual variability. Conclusions: These results support the potential of the EEG Switch as a reliable, non-motor-dependent cognitive assessment tool. The system is especially useful for populations with limited motor control, such as the elderly or individuals with physical disabilities. While Mild Cognitive Impairment (MCI) is an important future target for application, the present study involved only healthy adult participants. Future research should examine the sources of individual differences and validate EEG switches in clinical contexts, including clinical trials involving MCI and dementia patients. Our findings lay the groundwork for a novel and accessible approach for cognitive evaluation using neurophysiological data.

We have been developing a cognitive, brain -machine interface (BMI)-based, training system called the "Neurotrainer." This system was initially designed to detect averaged event -related potentials (ERPs), which reflect momentary heightened attention, allowing these individuals to participate in cognitive training without the need for hands-on interaction. In this study, we expanded our method to decode single -trial ERPs during a racing game, with the expectation that neurofeedback would accelerate the training process. We assessed the performance of the prototype system with healthy volunteers. The decoding accuracy of the target character was approximately 54% for a single trial and 83% for five trials (chance level = 12.5%). Moreover, ERP responses were stronger in the feedback condition than in the no -feedback condition. These results suggest that the BMI could be an effective tool for cognitive training, as real-time neurofeedback influences the brain activation of the players.

We have been developing a cognitive assessment system based on an electroencephalography (EEG)-based brain-machine interface (BMI) that uses event-related potentials (ERPs) as a virtual "Mind Switch." ERPs reflect temporal changes in attention and are also known as potential biomarkers for assessing cognitive function. We recorded EEG data from 10 healthy subjects who performed target-selection tasks based upon the visual discrimination of fingerspelling images with or without motor responses (key release). We found differential responses between target and non-target images, which were quantitatively evaluated by the decoding accuracy of the target. We also found that ERPs were enhanced when accompanied by key releases, which might indicate that a more attentive state was involved. These results suggest that fingerspelling images are a feasible tool for cognitive assessment. Future studies should be conducted to demonstrate the applicability of our system in preventing frailty in older people.

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 final goal is to promote brain health by developing a brain-machine interface (BMI)-based cognitive training device. We have been developing a cognitive level assessment device called the "Neurotrainer," which uses event-related potential (ERP) analysis. This device was operated by decoding single-trial ERPs using pattern recognition. Here, we extended our method to decoding single-trial ERPs during a racing game with 8 choices of characters. The decoding accuracy of the target character was about 54% for a single trial and 83% for a cumulative total of five trials (chance level = 12.5%). Also, the ERP responses were stronger in the feedback condition than in the no-feedback condition. These results suggest that the BMI might be an effective tool for cognitive training, in which real-time neurofeedback affects the activation of the brain of the players.

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 chapter reviews the recent progress in EEG technologies. We focused on EEG-based brain–machine/computer interfaces, especially the use of the event-related potential (ERP) as a Mind Switch for communication aid as well as cognitive assessment/training.

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.

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.

We have been developing an electroencephalography (EEG)-based brain-machine/computer interface (BMI/BCI) that uses event-related potentials (ERPs) as a "mind switch." ERP reflects a temporal change in attention and is also known as a potential biomarker for degrading cognitive functions, which can be applied to older people even with motor decline. In this study, we focused on this characteristic of ERP to develop a novel cognitive assessment system, "Neurodetector." This system was designed to evaluate a subject's cognitive function according to his/her success rate of a cognitive task performed by the mind switch. As the first step to establishing a proof of concept, we recorded EEG data from 40 healthy adult subjects (under 65 years old) during 3 cognitive tasks with varying difficulty levels. As a result, subjects could successfully control the mind switch (elicit detectable ERP) to perform all tasks beyond the chance level, although success rates varied among individuals. Furthermore, the average success rate for the 3 tasks gradually increased as the task became easier. These results suggest that the success rate is an efficient single-index that reflects the degree of cognitive load within a subject. Following this research, we will be studying the cause of differences between subjects to further explore this index's effectiveness as a cognitive biomarker for clinical assessment. As a future prospectus, we vision the Neurodetector to be applied for early detection of dementia.

We have been developing a cognitive assessment system to detect subtle symptoms of mild cognitive impairment. Here, we examined the feasibility to evaluate the spatial cognition by the sequential delayed matching-to-sample task, in which the participants were required to select the figure with the same angle as the sample. We prepared four groups of the figures (Landolt ring, back of the right hand, chicken and map of Hokkaido) with a variation of 8 rotation angles (0°, ±45°, ±90°, ±135°, 180°). Data from 20 normal adult participants were recorded as a control group for the future of clinical trials. The reaction time tended to be extended according to the complexity of the figures. It also changed depending upon the figure’s rotation angle, though the effect was not necessarily linear from their upright position. These results suggest that our task might contribute to evaluate a higher spatial cognitive ability in daily life.

We have been developing an EEG-based communication aid "Neurocommunicator" (NC) for people with severe motor disabilities. The NC utilizes an event-related potential (ERP), the cognitive component of the EEG. Here, we studied the feasibility of the NC for the cognitive assessment to detect subtle symptoms of mild cognitive impairment. We recorded the EEG data from 40 normal adult subjects, as the control group, during the target only task, the oddball target task, and the target selection task. We supposed that the task became more difficult in this order. While we observed the ERP to the targets in all tasks, the differential response between the target and the nontargets systematically changed by the task difficulty. These results suggest that the combination of these three tasks could have potential to reveal the cognitive processes such as bottom-up and top-down attention, which should be important to evaluate the cognitive impairment.

We focused on the feasibility of an EEG based cognitive training to prevent dementia in elderly people. As the first step, we attempted to develop the "Neurotrainer" that utilized the event-related potential (ERP) as a switch to control games. We recorded the ERP data from 11 normal adult subjects, during the oddball task with the game like the flash card using some clip arts, giving the feedback to the players depending on the level of the ERP. All players produced strong ERPs, and performed well at the level of about 82% of success rate. These results suggest that the Neurotrainer could be a good candidate of the cognitive training system, which can be commercialized as the "bSports."

In this study, we focused on brain activity that reflected the decision-making process of customers. We used the shopping simulation task as a model of daily shopping, in which a variety of product images were used as visual stimuli for the sequential delayed matching-to-sample paradigm. We recorded EEG data from 12 normal subjects and examined the event-related potentials (ERPs) in two conditions; the subject selected one (target) out of 8 products (nontargets) either “to buy” (positive condition) or “NOT to buy” (negative condition). We observed stronger ERPs to the targets than to the nontargets in both the positive and negative conditions. The magnitude of the response was, however, greater in the positive than negative condition especially around 400 ms after the stimulus presentation. These results suggest that such an enhance response of the ERP might be useful for the indicator of the purchasing intention of the customers.