Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2021, Volume and Issue: 29, P. 1998 - 2007

Published: Jan. 1, 2021

A brain-computer interface (BCI) provides a direct communication channel between brain and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has received increasing attention due to its high information transfer rate, which is accomplished by individual calibration for frequency recognition. Task-related component analysis (TRCA) recent state-of-the-art method individually calibrated SSVEP-BCIs. However, in TRCA, the spatial filter learned from each stimulus may be redundant temporal not fully utilized. To address this issue, paper proposes novel method, i.e., task-discriminant (TDCA), further improve performance of individually-calibrated SSVEP-BCI. The TDCA was evaluated two publicly available benchmark datasets, results demonstrated that outperformed ensemble TRCA other competing methods significant margin. An offline online experiment testing 12 subjects validated effectiveness TDCA. present study new perspective designing decoding SSVEP-BCI presents insight implementation high-speed speller applications.

Language: Английский

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Virtual-Reality Interpromotion Technology for Metaverse: A Survey

IEEE Internet of Things Journal, Journal Year: 2023, Volume and Issue: 10(18), P. 15788 - 15809

Published: April 10, 2023

The metaverse aims to build an immersive virtual reality world support the daily life, work, and recreation of people. In this survey, status quo is investigated, technical framework introduced from three aspects: 1) generation worlds; 2) connection real objects; 3) transmission data. Specifically, survey first discusses development challenges related technologies for methods 3-D generation; human–computer interaction experience; ecosystem. Second, we investigate extended (XR), motion capture, brain–computer interface evaluate potential research directions these entrance metaverse. Finally, network data are reviewed Internet Things (IoT), 5G/6G wireless, edge computing aspects, demand side in virtual-reality interpromotion, big processing, low-latency networking discussed, promising hotspots identified.

Language: Английский

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Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: July 14, 2023

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks even irreversible damage to soft tissues. Here, we propose in-ear visual auditory BCIs based on bioelectronics, named as SpiralE, can adaptively expand spiral along the meatus under electrothermal actuation ensure conformal contact. Participants achieve offline accuracies of 95% 9-target steady state evoked potential (SSVEP) BCI classification type target phrases successfully a calibration-free 40-target online SSVEP speller experiment. Interestingly, SSVEPs exhibit significant 2

Language: Английский

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Flexible Electrodes for Brain–Computer Interface System

Advanced Materials, Journal Year: 2023, Volume and Issue: 35(47)

Published: May 5, 2023

Abstract Brain–computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant applications. The restoration communication motor function, treatment psychological disorders, gaming, other daily therapeutic applications all benefit from BCI. electrodes hold key to essential, fundamental BCI precondition electrical brain activity detection delivery. However, traditional rigid are limited due their mismatch Young's modulus, potential damages human body, a decline signal quality with time. These factors make development flexible vital urgent. Flexible made soft materials grown popularity recent years as an alternative conventional because they offer greater conformance, for higher signal‐to‐noise ratio (SNR) signals, wider range Therefore, latest classifications future developmental directions fabricating these explored this paper further encourage speedy advent In summary, perspectives outlook developing discipline provided.

Language: Английский

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Cognitive Infocommunications

Studies in big data, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 31

Published: Jan. 1, 2024

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Nanomaterial-based microelectrode arrays for in vitro bidirectional brain–computer interfaces: a review

Microsystems & Nanoengineering, Journal Year: 2023, Volume and Issue: 9(1)

Published: Jan. 30, 2023

Abstract A bidirectional in vitro brain–computer interface (BCI) directly connects isolated brain cells with the surrounding environment, reads neural signals and inputs modulatory instructions. As a noninvasive BCI, it has clear advantages understanding exploiting advanced function due to simplified structure high controllability of ex vivo networks. However, core BCIs, microelectrode arrays (MEAs), urgently need improvements strength signal detection, precision modulation biocompatibility. Notably, nanomaterial-based MEAs cater all requirements by converging multilevel simultaneously applying stimuli at an excellent spatiotemporal resolution, as well supporting long-term cultivation neurons. This is enabled advantageous electrochemical characteristics nanomaterials, such their active atomic reactivity outstanding charge conduction efficiency, improving performance MEAs. Here, we review fabrication applied BCIs from interdisciplinary perspective. We also consider decoding coding activity through highlight various usages coupled dissociated cultures benefit future developments BCIs.

Language: Английский

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Deep Learning in EEG-Based BCIs: A Comprehensive Review of Transformer Models, Advantages, Challenges, and Applications

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 127271 - 127301

Published: Jan. 1, 2023

Brain-computer interfaces (BCIs) have undergone significant advancements in recent years. The integration of deep learning techniques, specifically transformers, has shown promising development research and application domains. Transformers, which were originally designed for natural language processing, now made notable inroads into BCIs, offering a unique self-attention mechanism that adeptly handles the temporal dynamics brain signals. This comprehensive survey delves transformers providing readers with lucid understanding their foundational principles, inherent advantages, potential challenges, diverse applications. In addition to discussing benefits we also address limitations, such as computational overhead, interpretability concerns, data-intensive nature these models, well-rounded analysis. Furthermore, paper sheds light on myriad BCI applications benefited from incorporation transformers. These span motor imagery decoding, emotion recognition, sleep stage analysis novel ventures speech reconstruction. review serves holistic guide researchers practitioners, panoramic view transformative landscape. With inclusion examples references, will gain deeper topic its significance field.

Language: Английский

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The metaverse in supply chain knowledge sharing and resilience contexts: An empirical investigation of factors affecting adoption and acceptance

Journal of Innovation & Knowledge, Journal Year: 2023, Volume and Issue: 8(4), P. 100446 - 100446

Published: Oct. 1, 2023

This study develops a conceptual framework based on the unified theory of acceptance and use technology, network perspective, metaverse characteristics. Data were gathered through questionnaires from 209 Chinese manufacturers, covariance-based structural equation modelling was main approach used. The results indicate that performance expectancy, facilitating conditions, establishing initial trust amongst supply chain partners can drive behavioural intention with respect to adoption for knowledge sharing improve resilience. In addition, sensory feedback is an important characteristic has critical influence behaviour. Moreover, it complementary partial mediation effect relationship between as well full mediating expectations. Finally, moderates expectancy. Our findings contribute literature in context

Language: Английский

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Global Adaptive Transformer for Cross-Subject Enhanced EEG Classification

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Journal Year: 2023, Volume and Issue: 31, P. 2767 - 2777

Published: Jan. 1, 2023

Due to the individual difference, EEG signals from other subjects (source) can hardly be used decode mental intentions of target subject. Although transfer learning methods have shown promising results, they still suffer poor feature representation or neglect long-range dependencies. In light these limitations, we propose Global Adaptive Transformer (GAT), an domain adaptation method utilize source data for cross-subject enhancement. Our uses parallel convolution capture temporal and spatial features first. Then, employ a novel attention-based adaptor that implicitly transfers domain, emphasizing global correlation features. We also use discriminator explicitly drive reduction marginal distribution discrepancy by against extractor adaptor. Besides, adaptive center loss is designed align conditional distribution. With aligned features, classifier optimized signals. Experiments on two widely datasets demonstrate our outperforms state-of-the-art methods, primarily due effectiveness These results indicate GAT has good potential enhance practicality BCI.

Language: Английский

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Cross-Subject Emotion Recognition Brain–Computer Interface Based on fNIRS and DBJNet

Cyborg and Bionic Systems, Journal Year: 2023, Volume and Issue: 4

Published: Jan. 1, 2023

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current on based fNIRS mainly limited within-subject, there lack related work across subjects. Therefore, this paper, we designed an evoking experiment with videos as stimuli constructed database. On basis, deep learning technology was introduced for first dual-branch joint network (DBJNet) constructed, creating ability generalize model new participants. The decoding performance obtained by proposed shows can effectively distinguish positive versus neutral negative emotions (accuracy 74.8%, F1 score 72.9%), 2-category task distinguishing 89.5%, 88.3%), 91.7%, 91.1%) proved powerful decode emotions. Furthermore, results ablation study structure demonstrate convolutional neural branch statistical achieve highest performance. paper expected facilitate development affective brain-computer interface.

Language: Английский

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