A Review of EEG-Based Brain-Computer Interface Systems Design

Zhang, Wenchang; Tan, Chuanqi; Sun, Fuchun; Wu, Haiyuan; Zhang, Bo

doi:10.26599/bsa.2018.9050010

Cited by 47 publications

(17 citation statements)

References 79 publications

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“…According to the situation of drug addicts, the training system of BCI can be used to improve the changes in brain function and can simultaneously help in improving attention and self‐control. Some researchers have reviewed the characteristics and application of the BCI technique, including the EEG signal processing technique, and sports imagination training, such as for stroke, which play a role in rehabilitation [41, 42]. Therefore, BCI is also a potential attempt in drug treatment.…”

Section: Existing Rehabilitation Methods and New Rehabilitation Tecmentioning

confidence: 99%

Review of pathological index detection and new rehabilitation technique of drug addicts

Yang

et al. 2020

Brain Science Advances

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There are two major research issues with regard to detoxification; one is pathological testing of drug users and the other is rehabilitation methods and techniques. Over the years, domestic and foreign researchers have done a lot of work on pathological changes in the brain and rehabilitation techniques for drug users. This article discusses the research status of these two aspects. At present, the evaluation of brain function in drug addicts is still dominated by a single electroencephalography (EEG), near-infrared spectroscopy (NIRS), or magnetic resonance imaging scan. The multimodal physiological data acquisition method based on EEG–NIRS technique is relatively advantageous for actual physiological data acquisition. The traditional drug rehabilitation method is based on medication and psychological counseling. In recent years, psychological correction (e.g., emotional ventilation, intelligent physical and mental decompression, virtual reality technique and drug addiction suppression system, sports training, and rehabilitation) and physical therapy (transcranial magnetic stimulation) have gradually spread. These rehabilitations focus on comprehensive treatment from the psychological and physical aspects. In recent years, new intervention ideas such as brain–computer interface technique have been continuously proposed. In this review, we have introduced multimodal brain function detection and rehabilitation intervention, which have theoretical and practical significance in drug rehabilitation research.

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Section: Existing Rehabilitation Methods and New Rehabilitation Tecmentioning

confidence: 99%

Review of pathological index detection and new rehabilitation technique of drug addicts

Yang

et al. 2020

Brain Science Advances

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“…Removing unrelated channels can improve spatial feature extraction. In time window setting, proper length of the signal segment should be cut out according to the mental activity tasks [18]. Aboalayon et al in their study reported many of the sleep stage detection schemes to employ pre-processing techniques before extracting the features from the signal, such as using frequency-selective-filtering and Discrete Wavelet Transform (DWT).…”

Section: Eeg Signal Processingmentioning

confidence: 99%

The Role of EEG in the Diagnosis and Management of Patients with Sleep Disorders

Behzad¹,

Behzad²

2021

JBBS

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Sleep disorders affect an individual's ability to sleep well on a regular and natural basis. Inadequate sleep can have adverse outcomes for health and safety. Electroencephalogram (EEG) has been presented as an authentic indicator to monitor brain activities. In this review paper, different procedures of EEG tests for recording and monitoring brain activity during sleep such as the EEG electrodes system and the Dreem headband (DH) have been introduced. Also, the processes of recording and analyzing the data have been discussed and compared with each other. The results of various stages of sleep from EEG tests help sleep specialists diagnose or evaluate sleep disorders accurately and choose appropriate strategies. Sleep disorder management is integral to provide patients with a safe sleeping environment.

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“…In this study, we developed a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video for stroke patients who were unable to move their upper limbs. Although it may cause tiredness due to flickering stimuli, a SSVEP-based BCI system has advantages such as no need training and higher classification accuracy compared to a motor-imagery based BCI system [22]. In stroke patients who have difficulties performing motor-imagery, the SSVEP triggering system can be more useful to induce repetitive movement through FES.…”

Section: Introductionmentioning

confidence: 99%

Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game

Son

Choi

Lim

et al. 2020

THC

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BACKGROUND: This study focused on developing an upper limb rehabilitation program. In this regard, a steady state visual evoked potential (SSVEP) triggered brain computer interface (BCI)-functional electrical stimulation (FES) based action observation game featuring a flickering action video was designed. OBJECTIVE: In particular, the synergetic effect of the game was investigated by combining the action observation paradigm with BCI based FES. METHODS: The BCI-FES system was contrasted under two conditions: with flickering action video and flickering noise video. In this regard, 11 right-handed subjects aged between 22–27 years were recruited. The differences in brain activation in response to the two conditions were examined. RESULTS: The results indicate that T3 and P3 channels exhibited greater Mu suppression in 8–13 Hz for the action video than the noise video. Furthermore, T4, C4, and P4 channels indicated augmented high beta (21–30 Hz) for the action in contrast to the noise video. Finally, T4 indicated suppressed low beta (14–20 Hz) for the action video in contrast to the noise video. CONCLUSION: The flickering action video based BCI-FES system induced a more synergetic effect on cortical activation than the flickering noise based system.

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A Review of EEG-Based Brain-Computer Interface Systems Design

Cited by 47 publications

References 79 publications

Review of pathological index detection and new rehabilitation technique of drug addicts

Review of pathological index detection and new rehabilitation technique of drug addicts

The Role of EEG in the Diagnosis and Management of Patients with Sleep Disorders

Development of a flickering action video based steady state visual evoked potential triggered brain computer interface-functional electrical stimulation for a rehabilitative action observation game

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