Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

Cho, Jeong-Hyun; Jeong, Ji-Hoon; Shim, Kyung-Hwan; Kim, Dong-Joo; Lee, Seong–Whan

doi:10.1109/smc.2018.00097

Cited by 51 publications

(30 citation statements)

References 12 publications

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“…The FPz and FCz channels were selected as ground and reference respectively. From these 64 channels, we selected 24 channels placed on the motor cortex [15], which are most relevant for the MI task (F3, F1, Fz, F2, F4, FC3, FC1, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4, P3, P1, Pz, P2, and P4). Impedances were measured between the electrodes and the scalp to maintain channels impedance below 15 kΩ.…”

Section: Data Descriptionmentioning

confidence: 99%

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Lee

Jeong

Shim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Electroencephalogram (EEG) motor imagery (MI) paradigm is widely used in non-invasive BCI to obtain encoded signals contained user intention of movement execution. However, EEG has intricate and non-stationary properties resulting in insufficient decoding performance. By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching. In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects. We propose an end-toend role assigned convolutional neural network (ERA-CNN) which considers discriminative features of each upper limb region by adopting the principle of a hierarchical CNN architecture. The proposed model outperforms previous methods on 3-class, 5-class and two different types of 7-class classification tasks. Hence, we demonstrate the possibility of decoding user intention by using only EEG signals with robust performance using an ERA-CNN.Index Terms-Brain-computer interface (BCI), Electroencephalogram (EEG), Motor imagery, Convolutional Neural Network (CNN)

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Section: Data Descriptionmentioning

confidence: 99%

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Lee

Jeong

Shim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Raw EEG signals filtered from 8 to 13 Hz with band-pass filter. The features of EEG signals should be extracted with common spatial pattern (CSP) [19]. Alpha band power was used in previous studies on visual motion imagery classification [20].…”

Section: Discussionmentioning

confidence: 99%

A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform

Kwon

Jeong

Kim

2020

2020 8th International Winter Conference on Brain-Computer Interface (BCI)

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In this study, 3D brain-computer interface (BCI) training platforms were used to stimulate the subjects for visual motion imagery and visual perception. We measured the activation brain region and alpha-band power activity when the subjects perceived and imagined the stimuli. Based on this, 4class were classified in visual stimuli session and visual motion imagery session respectively. The results showed that the occipital region is involved in visual perception and visual motion imagery, and alpha-band power is increased in visual motion imagery session and decreased in visual motion stimuli session. Compared with the performance of visual motion imagery and motor imagery, visual motion imagery has higher performance than motor imagery. The binary class was classified using one versus rest approach as well as analysis of brain activation to prove that visual-related brain wave signals are meaningful, and the results were significant.

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“…The system uses the regularized version of the linear discriminant analysis (RLDA) (Lotte and Guan, 2009) to distinguish between the target and non-target epochs. This binary model has been employed previously to detect P300 potentials (Zhumadilova et al, 2017) and classify other electrophysiological responses (Cho et al, 2018). In this stage, the classifier evaluates only a small subset Z = z 1 , z 2 , .…”

Section: Classificationmentioning

confidence: 99%

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

et al. 2020

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This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

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Classification of Hand Motions within EEG Signals for Non-Invasive BCI-Based Robot Hand Control

Cited by 51 publications

References 12 publications

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

Classification of High-Dimensional Motor Imagery Tasks Based on An End-To-End Role Assigned Convolutional Neural Network

A Novel Framework for Visual Motion Imagery Classification Using 3D Virtual BCI Platform

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

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