The ongoing joint project has made significant strides in advancing BCI technologies, with a focus on enhancing user interaction and performance. The research team is dedicated to improving the usability and effectiveness of various BCI systems, particularly regarding visual feedback mechanisms and intuitive control interfaces, including visual feedback mechanisms for cVEP-BCI systems to enhance user interaction and developing an intuitive object-control method for smart-home environments using cVEP flickering technology. Research is also being conducted on designing 3D stimuli for virtual reality applications utilizing the SSMVEP stimulus protocol, which aims to improve user engagement. A significant achievement includes the development of a VEP transaction approval system that integrates real-time user feedback, achieving high accuracy rates. A key objective of the project is to develop a collaborative BCI framework for motor imagery rehabilitation, which incorporates real-time cognitive state monitoring to enhance therapeutic interactions and facilitate the control of lower-limb exoskeletons.
Additional goals include designing a custom integrated circuit (IC) in 65 nm for concurrent measurements of EEG and electrode-skin impedance, conducting systematic reviews of hybrid EEG–EMG biosensing, deep learning, and VR/Serious Games, and developing a collaborative BCI system for motor imagery within a virtual reality rehabilitation environment. Knowledge exchange and collaboration are prioritized through seminars and guest lectures, ensuring effective dissemination of findings within the academic community.
Research activities also involve extensive data collection and analysis, leading to valuable insights that inform the design of future BCI applications. The team actively engages in various dissemination activities, including workshops and presentations, to share findings and methodologies with a broader audience.
In the field of human emotion recognition using a hybrid BCI, we conducted a systematic mapping study of recent work at the intersection of hybrid EEG–EMG biosensing, deep learning, and VR/Serious Games, screening 2,128 records and including 28 eligible studies. The analysis revealed that no existing work combines EEG+EMG with deep learning in VR, with evidence instead clustering in partial combinations. Based on synthesized pipelines and evaluation practices, we identified key gaps and proposed a roadmap, including VR-native EEG–EMG datasets and subject-independent benchmarking protocols.
Regarding AD biomarker development, we examined the relationship between simultaneously recorded scalp EEG and hippocampal activity using depth electrodes in patients with temporal lobe epilepsy (TLE), a population relevant to Alzheimer's disease (AD) research due to overlapping pathological features. In addition, we successfully exploited the potential of scalp EEG to identify individuals with amyloid and tau positivity, the two primary biomarkers of AD alongside neurodegeneration.
