Many real-world brain-computer interface (BCI) applications rely on single-trial classification of event-related potentials (ERPs) in EEG signals. However, because different subjects have different neural responses to even the same stimulus, it is very difficult to build a generic ERP classifier whose parameters fit all subjects. The classifier needs to be calibrated for each individual subject, using some labeled subject-specific data. This paper proposes both online and offline weighted adaptation regularization (wAR) algorithms to reduce this calibration effort, i.e., to minimize the amount of labeled subject-specific EEG data required in BCI calibration, and hence to increase the utility of the BCI system. We demonstrate using a visually-evoked potential oddball task and three different EEG headsets that both online and offline wAR algorithms significantly outperform several other algorithms. Moreover, through source domain selection, we can reduce their computational cost by about 50%, making them more suitable for real-time applications.
Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding
Institute of Signal Processing and System Theory, University of Stuttgart（信号处理与系统理论研究所，斯图加特大学）；Clinical Neurotechnology Laboratory, Department of Psychiatry and Neurosciences, Charité Campus Mitte (CCM), Charité - Universitätsmedizin Berlin（临床神经技术实验室，精神病学与神经科学系，Charité-柏林大学医学院）
Université de Versailles Saint-Quentin（凡尔赛圣康坦大学）；University Paris-Saclay（巴黎萨克雷大学）；Foxstream（Foxstream公司）；Cogitamus；CNRS（法国国家科学研究中心）
Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of…
EDAPT: Towards Calibration-Free BCIs with Continual Online Adaptation
Excellence Cluster Machine Learning, University of Tübingen（卓越集群机器学习中心，图宾根大学）；Tübingen AI Center, University of Tübingen（图宾根人工智能中心，图宾根大学）；Department of Neurology and Stroke, University of Tübingen（神经学与中风部门，图宾根大学）；Hertie Institute for Clinical Brain Research, University of Tübingen（赫尔特研究所，图宾根大学）；Department of Empirical Inference, Max Planck Institute for Intelligent Systems（经验推理部门，智能系统马克斯·普朗克研究所）；Hertie Institute for AI in Brain Health, Tübingen（赫尔特人工智能与脑健康研究所，图宾根）
T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs
Huazhong University of Science and Technology（华中科技大学）；School of Artificial Intelligence and Automation（人工智能与自动化学院）；Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）；Zhengzhou University（郑州大学）；School of Electrical and Information Engineering（电气工程学院）；School of Civil and Hydraulic Engineering（土木工程与水利工程学院）
Motor-Imagery based BCI (MI-BCI) neurorehabilitation can improve locomotor ability and reduce the deficit symptoms in Parkinson's Disease patients. Advanced Motor-Imagery BCI methods are needed to overcome the accuracy and time-related MI BCI calibration challenges in such patients. In this study, we proposed a Multi-session FBCSP (msFBCSP) based on inter-session transfer learning and we investigated its performance compared to the single-session based FBSCP. The main result of this study is the significantly improved accuracy obtained by proposed msFBCSP compared to single-session FBCSP in PD…
South China Normal University（华南师范大学）；Guangdong University of Finance（广东金融学院）；Pazhou Lab（琶洲实验室）
Brain-computer interface (BCI) provides a direct communication pathway between human brain and external devices. Before a new subject could use BCI, a calibration procedure is usually required. Because the inter- and intra-subject variances are so large that the models trained by the existing subjects perform poorly on new subjects. Therefore, effective subject-transfer and calibration method is essential. In this paper, we propose a semi-supervised meta learning (SSML) method for subject-transfer learning in BCIs. The proposed SSML learns a meta model with the existing subjects first, then fi…
Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces
Ministry of Education Key Laboratory of Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan, 430074 China（教育部长图像处理与智能控制重点实验室，华中科技大学，武汉，430074中国）；Zhongguancun Academy, Beijing, 100084 China（中关村学院，北京，100084中国）
Drowsiness reduces concentration and increases response time, which causes fatal road accidents. Monitoring drivers' drowsiness levels by electroencephalogram (EEG) and taking action may prevent road accidents. EEG signals effectively monitor the driver's mental state as they can monitor brain dynamics. However, calibration is required in advance because EEG signals vary between and within subjects. Because of the inconvenience, calibration has reduced the accessibility of the brain-computer interface (BCI). Developing a generalized classification model is similar to domain generalization, whi…
iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System
University of Technology Sydney（悉尼科技大学）；Australian AI Institute（澳大利亚人工智能研究院）；School of Computer Science, Faculty of Engineering and Information Technology（工程与信息技术学院计算机科学学院）
Huazhong University of Science and Technology（华中科技大学）；Shanghai Jiao Tong University（上海交通大学）
A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfrie…
The inter/intra-subject variability of electroencephalography (EEG) makes the practical use of the brain-computer interface (BCI) difficult. In general, the BCI system requires a calibration procedure to tune the model every time the system is used. This problem is recognized as a major obstacle to BCI, and to overcome it, approaches based on transfer learning (TL) have recently emerged. However, many BCI paradigms are limited in that they consist of a structure that shows labels first and then measures "imagery", the negative effects of source subjects containing data that do not contain cont…
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
DataNova；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；DCS Corp（DCS公司）；University of Technology, Sydney（悉尼科技大学）；National Chiao-Tung University（国立交通大学）
Boston University（波士顿大学）；Sabanci University（萨班哲大学）
Objective: Steady-state visually evoked potentials (SSVEPs), measured with EEG (electroencephalogram), yield decent information transfer rates (ITR) in brain-computer interface (BCI) spellers. However, the current high performing SSVEP BCI spellers in the literature require an initial lengthy and tiring user-specific training for each new user for system adaptation, including data collection with EEG experiments, algorithm training and calibration (all are before the actual use of the system). This impedes the widespread use of BCIs. To ensure practicality, we propose a highly novel target ide…
Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), which does not require calibration on the user. Methods: EEG signals were converted to spectrograms and served as input to train DCNNs using the transfer learning technique. We also modified and applied a data augmentation method, SpecAugment, generally employed for speech recognition. Furthermore, for comparison purposes, we classified the SSVEP dataset using Support-vector machi…
Leveraging Foundation Models for Calibration-Free c-VEP BCIs
Department of Biomedical Engineering, University of Calgary（生物医学工程系，卡尔加里大学）；Department of Electrical and Computer Engineering, Queen’s University（电气与计算机工程系，皇后大学）；Department of Pediatric and Clinical Neuroscience, University of Calgary（儿科与临床神经科学系，卡尔加里大学）；Department of Electrical and Software Engineering, University of Calgary（电气与软件工程系，卡尔加里大学）
Korea University（高丽大学）；Chungbuk National University（忠北国立大学）
Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjec…
Objective: This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach: We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and EEG montages). Main results: Study results verified the efficacy of LST in obviating the variability of SSVEPs when transf…
Riemannian Geometry-Based EEG Approaches: A Literature Review
Sapienza University of Rome（罗马第一大学）；University of Biskra（比斯克拉大学）；University of Djelfa（杰勒法大学）；Polytechnic Military School（军事理工学院）；Italian National Research Council（意大利国家研究委员会）；Czestochowa University of Technology（琴斯托霍瓦理工大学）
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
DataNova；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；DCS Corp（DCS公司）；National Chiao-Tung University（国立交通大学）；University of Technology, Sydney（悉尼科技大学）
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA（电气与计算机工程系，波士顿大学）；Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany（人类认知与脑科学Max Planck研究所，莱比锡，德国）；Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey（工程与自然科学学院，萨班奇大学，伊斯坦布尔，土耳其）
Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort
Department of Biostatistics and Bioinformatics（生物统计学与生物信息学系）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；Department of Physical Medicine and Rehabilitation（物理医学与康复系）；Department of Biomedical Engineering（生物医学工程系）；University of Michigan（密歇根大学）
ISAM-MTL: Cross-subject multi-task learning model with identifiable spikes and associative memory networks
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）
Channel Reflection: Knowledge-Driven Data Augmentation for EEG-Based Brain-Computer Interfaces
Huazhong University of Science and Technology（华中科技大学）；Shenzhen Huazhong University of Science and Technology Research Institute（华中科技大学深圳研究院）；China Electronic System Technology Co., Ltd.（中国电子系统技术有限公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology（华中科技大学土木与水利工程学院）
In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on decoding EEG were designed in a subject-specific manner by using calibration samples, with no concern of its practical use, hampered by time-consuming steps and a large data requirement. To this end, recent studies adopted a transfer learning strategy, especially domain adaptation techniques. Among those, to our knowledge, an adversarial learning …
Motor imagery brain computer interface designs are considered difficult due to limitations in subject-specific data collection and calibration, as well as demanding system adaptation requirements. Recently, subject-independent (SI) designs received attention because of their possible applicability to multiple users without prior calibration and rigorous system adaptation. SI designs are challenging and have shown low accuracy in the literature. Two major factors in system performance are the classification algorithm and the quality of available data. This paper presents a comparative study of …
Objective. Many electroencephalogram (EEG)-based brain-computer interface (BCI) systems use a large amount of channels for higher performance, which is time-consuming to set up and inconvenient for practical applications. Finding an optimal subset of channels without compromising the performance is a necessary and challenging task. Approach. In this article, we proposed a cross-correlation based discriminant criterion (XCDC) which assesses the importance of a channel for discriminating the mental states of different motor imagery (MI) tasks. Channels are ranked and selected according to the pr…
The University of Edinburgh（爱丁堡大学）；University of Ottawa（渥太华大学）；Politecnico di Torino（都灵理工大学）；Shahed University（沙希德大学）
A conventional brain-computer interface (BCI) requires a complete data gathering, training, and calibration phase for each user before it can be used. In recent years, a number of subject-independent (SI) BCIs have been developed. Many of these methods yield a weaker performance compared to the subject-dependent (SD) approach, and some are computationally expensive. A potential real-world application would greatly benefit from a more accurate, compact, and computationally efficient subject-independent BCI. In this work, we propose a novel subject-independent BCI framework, named CCSPNet (Convo…
Online Adaptation via Dual-Stage Alignment and Self-Supervision for Fast-Calibration Brain-Computer Interfaces
School of Information, Shanxi University of Finance and Economics（山西财经大学信息学院）；Chinese Academy of Sciences（中国科学院）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation（多模态人工智能系统国家重点实验室，自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）；Joint Laboratory of Intelligence Science and Technology, Institute of Systems Engineering, Macau University of Science and Technology（澳门科技大学智能科学与技术联合实验室）
