Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy

Abibullaev, Berdakh; An, Jinung; Lee, Seung Hyun; Moon, Jeon Il

doi:10.1016/j.measurement.2016.12.001

Cited by 15 publications

(8 citation statements)

References 51 publications

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“…CAS Filho et al used the graph method to classify human hands signals, achieving a high recognition rate of up to 98% [ 49 ]. Research shows that the recognition of motor information in brain signals using the BCI is an effective method [ 50 , 51 , 52 ]. Although it is very accurate to identify motion characteristics using EEG, the authors cannot confirm that it also has an efficient motion recognition rate when the auxiliary robot is controlled in real time outside the field of vision.…”

Section: Discussionmentioning

confidence: 99%

Study of the Home-Auxiliary Robot Based on BCI

Wang

Zhang

et al. 2018

Sensors

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A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person’s field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects’ electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated. Then, the motion characteristics are analyzed using the brain network parameters clustering coefficient (C) and global efficiency (G). Meanwhile, the eye movement characteristics in the F3 and F4 channels are identified. Finally, the motion characteristics identified by brain networks and eye movement characteristics can be used to control the home-auxiliary robot platform. The experimental result shows that the accuracy rate of left and right motion recognition using this method is more than 93%. Additionally, the similarity between that autonomous return path and the real path of the home-auxiliary robot reaches up to 0.89.

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Section: Discussionmentioning

confidence: 99%

Study of the Home-Auxiliary Robot Based on BCI

Wang

Zhang

et al. 2018

Sensors

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“…In addition, the EEG and NIRS measuring devices are small and movable and the measuring process is noninvasive and nonradioactive. To some extent, they can replace the traditional biochemical test or imaging diagnosis method [26][27][28]. Fig.…”

Section: Eeg-nirs Hardware and Synchronous Acquisition Methodsmentioning

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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“…ere are many researches on using human physiological signals to control external devices [51][52][53][54]. Roy et al used the genetic algorithm (GA) to recognize human left and right arm movements, and the recognition accuracy was 75.77% [55].Šumak et al successfully used eye wink, eyebrow motion, clenching of teeth, and smirk to control different functions of keyboard operation [7].…”

Section: Previous Studiesmentioning

confidence: 99%

Research on Home-Auxiliary Robot System Based on Characteristics of Human Physiological and Motion Signals

Wang

Zhang

2020

Complexity

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A home-auxiliary robot system based on characteristics of the electrooculogram (EOG) and tongue signal is developed in the current study, which can provide daily life assistance for people with physical mobility disabilities. It relies on five simple actions (blinking twice in a row, tongue extension, upward tongue rolling, and left and right eye movements) of the human head itself to complete the motions (moving up/down/left/right and double-click) of a mouse in the system screen. In this paper, the brain network and BP neural network algorithms are used to identify these five types of actions. The result shows that, for all subjects, their average recognition rates of eye blinks and tongue movements (tongue extension and upward tongue rolling) were 90.17%, 88.00%, and 89.83%, respectively, and after training, the subjects can complete the five types of movements in sequence within 12 seconds. It means that people with physical disabilities can use the system to quickly and accurately complete life self-help, which brings great convenience to their lives.

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Design and evaluation of action observation and motor imagery based BCIs using Near-Infrared Spectroscopy

Cited by 15 publications

References 51 publications

Study of the Home-Auxiliary Robot Based on BCI

Study of the Home-Auxiliary Robot Based on BCI

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

Research on Home-Auxiliary Robot System Based on Characteristics of Human Physiological and Motion Signals

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