Brain–Computer Interfaces

He, Bin; Yuan, Han; Meng, Jianjun; Gao, Shangkai

doi:10.1007/978-3-030-43395-6_4

Cited by 83 publications

(51 citation statements)

References 183 publications

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“…Over the last few decades, several neuroengineering and neuroscience studies have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into commands that control an application or device (McFarland and Wolpaw, 2011 ; Chaudhary et al, 2016 ; Abiri et al, 2019 ; He et al, 2020 ). While many studies have demonstrated the theoretic potential of BCI, especially by deploying novel machine learning methods for detecting distinct task-specific attributes of the brain, a point of concern that remains is that the studies are still confined to lab settings and mostly limited to healthy able-bodied subjects (Lotte et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…A major proportion of BCI literature has focused on improving performance of BCI applications by enhancing the decoding performance of signal processing and machine learning algorithms (He et al, 2020 ). While this is an important contributing factor, research has also demonstrated that mutual learning of the machine and the user is critical for a successful closed-loop implementation of BCI (Perdikis et al, 2018 ; Perdikis and Millan, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…Cybathlon game rules also required to confirm that the control was achieved solely by using BCI and that ocular or muscular artifacts do not impact performance. Considering the susceptibility of EEG to electronic noise and physiological artifacts arising from sources other than the brain, such as eye movements (He et al, 2020 ), the BCI design also consisted of pre-processing, including a real-time artifact detection and correction.…”

Section: Introductionmentioning

confidence: 99%

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Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

Robinson

Chouhan

Mihelj

et al. 2021

Front. Hum. Neurosci.

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Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

Robinson

Chouhan

Mihelj

et al. 2021

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Decades of research have sought to find alternative methods of communication between the human brain and the outside world. With the ever-growing knowledge in the neuroscience field, scientists have designed the brain–computer interface (BCI) to achieve this goal ( Wolpaw et al, 2002 ; He et al, 2020 ). A BCI attempts to recognize the user’s intent by decoding her/his neurophysiological signals and then converts this intent into commands to control objects, such as a cursor on a computer screen ( Wolpaw et al, 1991 ; Trejo et al, 2006 ), a quadcopter ( LaFleur et al, 2013 ), or a robotic arm in space ( Meng et al, 2016 ; Edelman et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we focus on the sensorimotor rhythm (SMR)-based BCI, which uses electroencephalogram (EEG) to detect scalp electrical signals and decode motor intention ( Yuan and He, 2014 ; He et al, 2015 ). The EEG-based SMR BCI has multiple merits, such as non-invasiveness, ease of use, relatively low cost, and high temporal resolution ( He et al, 2020 ). This is particularly true when only a few electrodes are used ( Clerc et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

et al. 2021

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Sensorimotor rhythm (SMR)-based brain–computer interfaces (BCIs) provide an alternative pathway for users to perform motor control using motor imagery. Despite the non-invasiveness, ease of use, and low cost, this kind of BCI has limitations due to long training times and BCI inefficiency—that is, the SMR BCI control paradigm may not work well on a subpopulation of users. Meditation is a mental training method to improve mindfulness and awareness and is reported to have positive effects on one’s mental state. Here, we investigated the behavioral and electrophysiological differences between experienced meditators and meditation naïve subjects in one-dimensional (1D) and two-dimensional (2D) cursor control tasks. We found numerical evidence that meditators outperformed control subjects in both tasks (1D and 2D), and there were fewer BCI inefficient subjects in the meditator group. Finally, we also explored the neurophysiological difference between the two groups and showed that the meditators had a higher resting SMR predictor, more stable resting mu rhythm, and a larger control signal contrast than controls during the task.

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Sustainable development of electroencephalography materials and technology

Xiong,

Li,

Luo

et al. 2024

SusMat

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Electroencephalogram (EEG) is one of the most important bioelectrical signals related to brain activity and plays a crucial role in clinical medicine. Driven by continuously expanding applications, the development of EEG materials and technology has attracted considerable attention. However, systematic analysis of the sustainable development of EEG materials and technology is still lacking. This review discusses the sustainable development of EEG materials and technology. First, the developing course of EEG is introduced to reveal its significance, particularly in clinical medicine. Then, the sustainability of the EEG materials and technology is discussed from two main aspects: integrated systems and EEG electrodes. For integrated systems, sustainability has been focused on the developing trend toward mobile EEG systems and big‐data monitoring/analyzing of EEG signals. Sustainability is related to miniaturized, wireless, portable, and wearable systems that are integrated with big‐data modeling techniques. For EEG electrodes and materials, sustainability has been comprehensively analyzed from three perspectives: performance of different material/structural categories, sustainable materials for EEG electrodes, and sustainable manufacturing technologies. In addition, sustainable applications of EEG have been presented. Finally, the sustainable development of EEG materials and technology in recent decades is summarized, revealing future possible research directions as well as urgent challenges.

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Brain–Computer Interfaces

Cited by 83 publications

References 183 publications

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot

Effects of Long-Term Meditation Practices on Sensorimotor Rhythm-Based Brain-Computer Interface Learning

Sustainable development of electroencephalography materials and technology

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