Asynchronous Brain-computer Interface Intelligent Wheelchair System Based on Alpha Wave and SSVEP EEG Signals

Nuo, Gao; Wenwen, Zhai; Lu, Shouyin

doi:10.1109/siprocess.2019.8868408

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…where, I is spectrogram intensity (in dB) Median frequency, median(f) Spectral Power Density [25] x i n ðÞ¼xnþ iD ðÞ , n ¼ 0,1,2, … , M À 1 (15) while; i = 0, 1, 2, … , L-1;…”

Section: Feature Extractionmentioning

confidence: 99%

“…It uses tags to localize itself incorporating computer vision, but suffers low maneuverability and unable to handle dynamics obstacles accurately in real time. An omni-directional moveable wheelchair design is proposed based on Mecanum wheel [15]. It is based on SSVEP and Alpha-wave based asynchronous control but the major disadvantage is non-autonomous and requires constant attention for the control mechanism.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EEG Control of a Robotic Wheelchair

Chaudhary¹,

Gupta²,

Kumar³

et al. 2023

Human-Robot Interaction - Perspectives and Applications

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The Brain-Computer Interface (BCI) technology has been widely used in clinical research; however, its adoption in consumer devices has been hindered by high costs, poor reliability and limited autonomy. In this study, we introduce a low-cost, open-source hardware-based, consumer-grade product that brings BCI technologies closer to the elderly and motor-impaired individuals. Specifically, we developed an autonomous motorized wheelchair with BCI-based input capabilities. The system employs the ROS-backend navigation stack, which integrates RTAB-MAP for mapping, localization, and visual odometry, as well as A* global and DWA local path planning algorithms for seamless indoor autonomous operations. Data acquisition is accomplished using OpenBCI 16-channel EEG sensors, while Ensemble-Subspace KNN machine learning model is utilized for intent prediction, particularly goal selection. The system offers active obstacle avoidance and mapping in all environments, while a hybrid BCI Motor Imagery based control is implemented in a known mapped environment. This prototype offers remarkable autonomy while ensuring user safety and granting unparalleled independent mobility to the motor-impaired and elderly.

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“…where, I is spectrogram intensity (in dB) Median frequency, median(f) Spectral Power Density [25] x i n ðÞ¼xnþ iD ðÞ , n ¼ 0,1,2, … , M À 1 (15) while; i = 0, 1, 2, … , L-1;…”

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Control of a Robotic Wheelchair

Chaudhary¹,

Gupta²,

Kumar³

et al. 2023

Human-Robot Interaction - Perspectives and Applications

View full text Add to dashboard Cite

show abstract

“…Brain-computer interfaces (BCIs) comprise a revolutionary human-computer interface that aims to enable direct communication between the brain and external devices by bypassing peripheral nerve and muscle activity, with the goal of improving the quality of life and productivity [1][2][3][4][5][6]. The steady-state-visual-evoked-potential (SSVEP)-based BCI is a significant class of BCI with potential applications [7].…”

Section: Introductionmentioning

confidence: 99%

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph

et al. 2023

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The steady-state visually evoked potential (SSVEP) is an important type of BCI that has various potential applications, including in virtual environments using virtual reality (VR). However, compared to VR research, the majority of visual stimuli used in the SSVEP-BCI are plane stimulation targets (PSTs), with only a few studies using stereo stimulation targets (SSTs). To explore the parameter optimization of the SSVEP-BCI virtual SSTs, this paper presents a parameter knowledge graph. First, an online VR stereoscopic stimulation SSVEP-BCI system is built, and a parameter dictionary for VR stereoscopic stimulation parameters (shape, color, and frequency) is established. The online experimental results of 10 subjects under different parameter combinations were collected, and a knowledge graph was constructed to optimize the SST parameters. The best classification performances of the shape, color, and frequency parameters were sphere (91.85%), blue (94.26%), and 13Hz (95.93%). With various combinations of virtual reality stereo stimulation parameters, the performance of the SSVEP-BCI varies. Using the knowledge graph of the stimulus parameters can help intuitively and effectively select appropriate SST parameters. The knowledge graph of the stereo target stimulation parameters presented in this work is expected to offer a way to convert the application of the SSVEP-BCI and VR.

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Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous

Wang

Yan

et al. 2021

IEEE Access

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Brain-computer interface (BCI) is a novel human-computer interaction model, which does not depend on the conventional output pathway (peripheral nerve and muscle tissue). In the past three decades, it has attracted the interest of researchers and gradually become a research hotspot. As a typical BCI application, the brain-controlled wheelchair (BCW) could provide a new communicating channel with the external environment for physically disabled people. However, the main challenge of BCW is how to decode multi-degree of freedom control instruction from electroencephalogram (EEG) as soon as possible. The research progress of BCW has been developed rapidly over the past fifteen years. In this review, we investigate the BCW from multiple perspectives, include the type of signal acquisition, the pattern of commands for the control system and the working mechanism of the control system. Furthermore, we summarize the development trend of BCW based on the previous investigation, and it is mainly manifested in three aspects: from a wet electrode to dry electrode, from single-mode to multi-mode, and from synchronous control to asynchronous control. With the continuous development of BCW, we also find new functions have been introduced into BCW to increase its stability and robustness. It is believed that BCW will be able to enter the real-life from the laboratory and will be widely used in rehabilitation medicine in the future.

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Asynchronous Brain-computer Interface Intelligent Wheelchair System Based on Alpha Wave and SSVEP EEG Signals

Cited by 3 publications

References 12 publications

EEG Control of a Robotic Wheelchair

EEG Control of a Robotic Wheelchair

Optimization of SSVEP-BCI Virtual Reality Stereo Stimulation Parameters Based on Knowledge Graph

Brain-Controlled Wheelchair Review: From Wet Electrode to Dry Electrode, From Single Modal to Hybrid Modal, From Synchronous to Asynchronous

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