Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

Lu-jia, Zhou; Tao, Xuewen; He, Feng; Zhou, Peng; Qi, Hongzhi

doi:10.1109/jbhi.2021.3066610

Cited by 4 publications

(4 citation statements)

References 41 publications

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“…Because of the low task specificity of ERD, only considering ERD features can easily lead to a misidentification of the task [50]. Many studies have introduced SSSEP features alongside the bilateral stimuli [23], [29] or asked subjects to perform selective sensory tasks alongside the motor imagery [25], [27] to enhance overall MI performance.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm

Zhang

Song

Zeng

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Vibration stimulation has been shown to have the potential to improve the activation pattern of unilateral motor imagery (MI) and to promote motor recovery. However, in the widely used left and right hand MI brain-computer interface (BCI) paradigm, the vibration stimuli cannot be directly applied to the imaginary side due to the spontaneity of imagery. In this study, we proposed a method of phase-dependent closed-loop vibration stimulation to be applied on both hands, and explored the effects of different vibration stimuli on the left and right hand MI-BCI. Eighteen healthy subjects were recruited and asked to perform, in sequence, MI tasks under three different conditions of vibratory feedback, which were no vibration stimulus (MI), phase-dependent closedloop vibration stimulus (PDS), and continuous vibration stimulus (CS). Then the performance of the left and right hand MI-BCI and the patterns of brain oscillation were compared and analyzed under these different stimulation conditions. The results showed that vibration stimulation effectively boosted the activation of the sensorimotor cortex and enhanced the functional connectivity among sensorimotor-related brain regions during MI. The closedloop stimulation evoked stronger event-related desynchronization patterns on the contralateral side of the imagined hand compared to continuous stimulation. There was a more obvious distinction between left hand task and right hand task. In addition, phasedependent closed-loop vibration stimulation increased classification accuracy by approximately 7% (paired t-test, p=0.004, n=18) compared to MI alone, while continuous vibration stimulation only increased it by 4% (paired t-test, p=0.067, n=18). This result further demonstrated the effectiveness of the phase-dependent closedloop vibration stimulation method in improving the overall performance of the MI paradigm and is expected to be further applied in areas such as stroke rehabilitation in the future.

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Section: Discussionmentioning

confidence: 99%

The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm

Zhang

Song

Zeng

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…SSSEP has the advantage of being easier to detect in the EEG, thanks to the use of exogenous stimuli, than MI-related cortical activities. False command triggering, caused by the detection of irrelevant mental activity during MI, can also be mitigated by the use of SSSEP [49].…”

Section: Discussionmentioning

confidence: 99%

“…3-class problems are rarely tackled, and BCI performance shows higher variability, with an accuracy from 42.1% to 80.93%. One study uses SSSEP to reduce the number of false triggering in MI-based BCI [49]. Comparison of SSSEP-based BCI with paradigms other than motor imagery have been scarcely performed, which reinforces our idea that the field should be further explored.…”

Section: Comparison Of Sssep-based Bci With a Somatosensory-evoked P3...mentioning

confidence: 99%

EEG-based brain–computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review

2021

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A brain–computer interface (BCI) aims to derive commands from the user’s brain activity in order to relay them to an external device. To do so, it can either detect a spontaneous change in the mental state, in the so-called ‘active’ BCIs, or a transient or sustained change in the brain response to an external stimulation, in ‘reactive’ BCIs. In the latter, external stimuli are perceived by the user through a sensory channel, usually sight or hearing. When the stimulation is sustained and periodical, the brain response reaches an oscillatory steady-state that can be detected rather easily. We focus our attention on electroencephalography-based BCIs (EEG-based BCI) in which a periodical signal, either mechanical or electrical, stimulates the user skin. This type of stimulus elicits a steady-state response of the somatosensory system that can be detected in the recorded EEG. The oscillatory and phase-locked voltage component characterising this response is called a steady-state somatosensory-evoked potential (SSSEP). It has been shown that the amplitude of the SSSEP is modulated by specific mental tasks, for instance when the user focuses their attention or not to the somatosensory stimulation, allowing the translation of this variation into a command. Actually, SSSEP-based BCIs may benefit from straightforward analysis techniques of EEG signals, like reactive BCIs, while allowing self-paced interaction, like active BCIs. In this paper, we present a survey of scientific literature related to EEG-based BCI exploiting SSSEP. Firstly, we endeavour to describe the main characteristics of SSSEPs and the calibration techniques that allow the tuning of stimulation in order to maximise their amplitude. Secondly, we present the signal processing and data classification algorithms implemented by authors in order to elaborate commands in their SSSEP-based BCIs, as well as the classification performance that they evaluated on user experiments.

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“…B RAIN computer interface (BCI) system can directly communicate with external equipment without relying on muscles and nerves tissue. BCI can be applied in many fields, such as helping patients with spinal cord injury, amyotrophic lateral sclerosis, atresia syndrome and other patients with brain-control equipment [1], [2], [3], [4], improving their ability to take care of themselves and communicate with people [5], [6], [7], and helping stroke patients with rehabilitation training [8], [9], [10]. It also has great potential in the game field [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding

Wang

Gong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain computer interface (BCI) is a system that directly uses brain neural activities to communicate with the outside world. Recently, the decoding of the human upper limb based on electroencephalogram (EEG) signals has become an important research branch of BCI. Even though existing research models are capable of decoding upper limb trajectories, the performance needs to be improved to make them more practical for real-world applications. This study is attempt to reconstruct the continuous and nonlinear multi-directional upper limb trajectory based on Chinese sign language. Here, to reconstruct the upper limb motion trajectory effectively, we propose a novel Motion Trajectory Reconstruction Transformer (MTRT) neural network that utilizes the geometric information of human joint points and EEG neural activity signals to decode the upper limb trajectory. Specifically, we use human upper limb bone geometry properties as reconstruction constraints to obtain more accurate trajectory information of the human upper limbs. Furthermore, we propose a MTRT neural network based on this constraint, which uses the shoulder, elbow, and wrist joint point information and EEG signals of brain neural activity during upper limb movement to train its parameters. To validate the model, we collected the synchronization information of EEG signals and upper limb motion joint points of 20 subjects. The experimental results show that the reconstruction model can accurately reconstruct the motion trajectory of the shoulder, elbow, and wrist of the upper limb, achieving superior performance than the compared methods. This research is very meaningful to decode the limb motion parameters for BCI, and it is inspiring for the motion decoding of other limbs and other joints.

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Reducing False Triggering Caused by Irrelevant Mental Activities in Brain-Computer Interface Based on Motor Imagery

Cited by 4 publications

References 41 publications

The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm

The Effects of Bilateral Phase-Dependent Closed-Loop Vibration Stimulation With Motor Imagery Paradigm

EEG-based brain–computer interfaces exploiting steady-state somatosensory-evoked potentials: a literature review

MTRT: Motion Trajectory Reconstruction Transformer for EEG-Based BCI Decoding

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