A Wireless BCI and BMI System for Wearable Robots

He, Wei; Zhao, Yue; Tang, Haoyue; Sun, Changyin; Fu, Wei

doi:10.1109/tsmc.2015.2506618

Cited by 68 publications

(24 citation statements)

References 64 publications

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“…Guger et al [57] used CSP to analyze EEG in real-time for providing feedback to the subject. Recently He et al [58] proposed a upper limb robotic arm control BCI-BMI system. He obtained the maximum classification accuracy of 91.7%.…”

Section: Common Spatial Pattern (Csp)mentioning

confidence: 99%

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

Rahman¹,

Joadder²

2017

Appl. Theory Comput. Technol.

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Motor Imagery (MI) is a voluntary modulation of brain signals for specific action without real limb movement. It is essential to classify MI signal to design a brain computer interface (BCI). BCI involves a number of signal processing steps, and a lot of techniques have been developed for each step. There can be numerous combinations of these techniques at different steps that can be employed to design a BCI. This work focuses on MI-based BCI using EEG signal and reviews the existing techniques. More importantly, a detailed comparative study is performed to explore the important combinations of methods by comparing their performance quantitatively. Often a method, which performs very good in one combination, can be bad performer in other combinations and it is a dilemma for the researchers to select appropriate methods for their desired BCI application.In our performance analysis, we have systematically included the variations of methods in each step of BCI such that it gives idea to BCI researchers how each method in one step fits best with specific combinations of methods in other steps. We have shown that how much each step is sensitive towards overall performance of the BCI system.We hope that this work helps, especially for new researchers, to provide a better guideline for designing more efficient BCI system.

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Section: Common Spatial Pattern (Csp)mentioning

confidence: 99%

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

Rahman¹,

Joadder²

2017

Appl. Theory Comput. Technol.

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show abstract

“…In designing bio-inspired sensing/actuation, He et al [58] proposed a wireless BCI-BMI system for an upper limb robotic arm. The EEG signals are processed via several wavelet denoising methods, common spatial pattern algorithm, and linear discriminant analysis algorithm.…”

Section: Guide To This Special Issuementioning

confidence: 99%

Guest Editorial An Overview of Biomedical Robotics and Bio-Mechatronics Systems and Applications

Yang

Burdet

2016

IEEE Trans. Syst. Man Cybern, Syst.

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“…erefore, BCIs can be used for helping patients with severe brain disorders or muscle damages to regain their ability to communicate directly with the outside environment through the brain electrophysiology response [1][2][3]. BCI can also be beneficial for the elderly as advanced assistive and rehabilitative technologies and useful for young able-bodied for controlling video games for entertainment [4,5] or controlling a robotic arm for several purposes [6][7][8][9]. However, most of the traditional brain-computer interface equipment is expensive, bulky, and tedious, which makes it difficult to popularize and apply brain-computer interface technology in real life.…”

Section: Introductionmentioning

confidence: 99%

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

Shao

Zhang

Belkacem

et al. 2020

Journal of Healthcare Engineering

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The assistive, adaptive, and rehabilitative applications of EEG-based robot control and navigation are undergoing a major transformation in dimension as well as scope. Under the background of artificial intelligence, medical and nonmedical robots have rapidly developed and have gradually been applied to enhance the quality of people’s lives. We focus on connecting the brain with a mobile home robot by translating brain signals to computer commands to build a brain-computer interface that may offer the promise of greatly enhancing the quality of life of disabled and able-bodied people by considerably improving their autonomy, mobility, and abilities. Several types of robots have been controlled using BCI systems to complete real-time simple and/or complicated tasks with high performances. In this paper, a new EEG-based intelligent teleoperation system was designed for a mobile wall-crawling cleaning robot. This robot uses crawler type instead of the traditional wheel type to be used for window or floor cleaning. For EEG-based system controlling the robot position to climb the wall and complete the tasks of cleaning, we extracted steady state visually evoked potential (SSVEP) from the collected electroencephalography (EEG) signal. The visual stimulation interface in the proposed SSVEP-based BCI was composed of four flicker pieces with different frequencies (e.g., 6 Hz, 7.5 Hz, 8.57 Hz, and 10 Hz). Seven subjects were able to smoothly control the movement directions of the cleaning robot by looking at the corresponding flicker using their brain activity. To solve the multiclass problem, thereby achieving the purpose of cleaning the wall within a short period, the canonical correlation analysis (CCA) classification algorithm had been used. Offline and online experiments were held to analyze/classify EEG signals and use them as real-time commands. The proposed system was efficient in the classification and control phases with an obtained accuracy of 89.92% and had an efficient response speed and timing with a bit rate of 22.23 bits/min. These results suggested that the proposed EEG-based clean robot system is promising for smart home control in terms of completing the tasks of cleaning the walls with efficiency, safety, and robustness.

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A Wireless BCI and BMI System for Wearable Robots

Cited by 68 publications

References 64 publications

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

A Review on the Components of EEG-based Motor Imagery Classification with Quantitative Comparison

Guest Editorial An Overview of Biomedical Robotics and Bio-Mechatronics Systems and Applications

EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface

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