Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407624 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308506 | PLOS |
J Neurosci Methods
February 2025
School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.
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October 2024
Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300192 China.
Brain-computer interface (BCI)-based robot combines BCI and robotics technology to realize the brain's intention to control the robot, which not only opens up a new way for the daily care of the disabled individuals, but also provides a new way of communication for normal people. However, the existing systems still have shortcomings in many aspects such as friendliness of human-computer interaction, and interaction efficient. This study developed a humanoid robot control system by integrating an augmented reality (AR)-based BCI with a simultaneous localization and mapping (SLAM)-based scheme for autonomous indoor navigation.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
November 2024
SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed.
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November 2024
Institute for Digital Technologies, Loughborough University London, London E20 3BS, UK.
Advances in brain-computer interfaces (BCIs) have enabled direct and functional connections between human brains and computing systems. Recent developments in artificial intelligence have also significantly improved the ability to detect brain activity patterns. In particular, using steady-state visual evoked potentials (SSVEPs) in BCIs has enabled noticeable advances in human activity monitoring and identification.
View Article and Find Full Text PDFRSC Adv
October 2024
State Key Laboratory of Metal Matrix Composites, Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China
Brain-computer interfaces (BCIs) provide promising prospects for the field of healthcare and rehabilitation, presenting significant advantages for humanity. The development of electrodes that exhibit satisfactory performance characteristics, including high electrical conductivity, optimal comfort, and exceptional stability, is crucial for the effective implementation of electroencephalography (EEG) recording in BCI systems. The present study introduces a novel EEG electrode design that utilizes a composite material consisting of reduced graphene oxide (RGO) and polyurethane (PU) sponge.
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