Immediate effects of short-term meditation on sensorimotor rhythm-based brain–computer interface performance

Kim, Jeehyun; Jiang, Xiyuan; Forenzo, Dylan; Liu, Yixuan; Anderson, Nancy; Greco, Carol M.; He, Bin

doi:10.3389/fnhum.2022.1019279

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…For this purpose, we chose to use the AR Signal Processing module, part of the standard BCI2000 software. This class of decoder is well established for BCI experiments (21,(48)(49)(50)(51) and does not require any training data before use, allowing us to implement it for each subject's first session. More information about the implementations of the traditional decoder and two DL decoders used here can be found in the Supplementary Materials.…”

Section: E Traditional Decodermentioning

confidence: 99%

Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface

Forenzo,

Zhu,

Shanahan

et al. 2023

Preprint

Self Cite

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Brain-computer interfaces (BCI) using electroencephalography (EEG) provide a non-invasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the lives of both healthy and motor impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep-learning (DL)-based decoders for online Continuous Pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a new labelling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human subjects, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and mid-session training to reduce inter-session variability. The results from these experiments show that pre-training did not significantly improve performance, but updating the models mid-session may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help improve the lives of both healthy individuals and motor impaired patients.

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Section: E Traditional Decodermentioning

confidence: 99%

Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface

Forenzo,

Zhu,

Shanahan

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Continuous tracking using deep learning-based decoding for noninvasive brain–computer interface

Forenzo,

Zhu,

Shanahan

et al. 2024

PNAS Nexus

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Brain–computer interfaces (BCI) using electroencephalography provide a noninvasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the quality of lives of healthy and motor-impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep learning (DL)-based decoders for online continuous pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a labeling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human participants, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pretraining models on data from other subjects, and midsession training to reduce intersession variability. The results from these experiments showed that pretraining did not significantly improve performance, but updating the models’ midsession may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help to improve the quality of lives of healthy and motor-impaired individuals.

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Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user – previous evidence, self-relevance and the user’s self-concept as potential superordinate human factors of relevance

Herbert

2024

Front. Hum. Neurosci.

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Brain–computer interfaces (BCIs) are well-known instances of how technology can convert a user’s brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user’s internal mental states and traits including motivation, affect or cognition, personality traits, or the user’s satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user’s self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user’s self-concept (self-referential knowledge and beliefs about one’s self) guide information processing and modulate the user’s motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one’s self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user’s self-concept in the BCI setting – including paradigms, user instructions, and training sessions.

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Immediate effects of short-term meditation on sensorimotor rhythm-based brain–computer interface performance

Cited by 3 publications

References 54 publications

Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface

Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface

Continuous tracking using deep learning-based decoding for noninvasive brain–computer interface

Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user – previous evidence, self-relevance and the user’s self-concept as potential superordinate human factors of relevance

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