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Reducing False Triggering Caused by Irrelevant Mental Activities in brain-computer interface Based on Motor Imagery

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第9期25卷 3638-3648页

作者： Zhou, Lujia Tao, Xuewen He, Feng Zhou, Peng Qi, Hongzhi Tianjin Univ Coll Precis Instrument & Optoelect Engn Dept Biomed Engn Tianjin 300072 Peoples R China Tianjin Univ Acad Med Engn & Translat Med Tianjin 300072 Peoples R China

In recent years, the brain-computer interface (BCI) based on motor imagery (MI) has been considered as a potential post-stroke rehabilitation technology. However, the recognition of MI relies on the event-related desynchronization (ERD) feature, which has poor task specificity. Further, there is the problem of false triggering (irrelevant mental activities recognized as the MI of the target limb). In this paper, we discuss the feasibility of reducing the false triggering rate using a novel paradigm, in which the steady-state somatosensory evoked potential (SSSEP) is combined with the MI (MI-SSSEP). Data from the target (right hand MI) and nontarget task (rest) were used to establish the recognition model, and three kinds of interference tasks were used to test the false triggering performance. In the MI-SSSEP paradigm, ERD and SSSEP features modulated by MI could be used for recognition, while in the MI paradigm, only ERD features could be used. The results showed that the false triggering rate of interference tasks with SSSEP features was reduced to 29.3%, which was far lower than the 55.5% seen under the MI paradigm with ERD features. Moreover, in the MI-SSSEP paradigm, the recognition rate of the target and nontarget task was also significantly improved. Further analysis showed that the specificity of SSSEP was significantly higher than that of ERD (p < 0.05), but the sensitivity was not significantly different. These results indicated that SSSEP modulated by MI could more specifically decode the target task MI, and thereby may have potential in achieving more accurate rehabilitation training.

关键词： Task analysis Interference Decoding Electroencephalography Induction motors Feature extraction Electrical stimulation brain-computer interface false triggering motor imagery steady-state somatosensory evoked potential task specificity

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Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: three-class classification of rest, right-, and left-hand motor execution

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NEUROPHOTONICS 2018年第1期5卷 011008页

作者： Trakoolwilaiwan, Thanawin Behboodi, Bahareh Lee, Jaeseok Kim, Kyungsoo Choi, Ji-Woong Daegu Gyeongbuk Inst Sci & Technol Dept Informat & Commun Engn Daegu South Korea

The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.

关键词： functional near-infrared spectroscopy brain-computer interface support vector machine artificial neural network convolutional neural network feature extraction

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CogniDron-EEG: A system based on a brain-computer interface and a drone for cognitive training

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COGNITIVE SYSTEMS RESEARCH 2023年 78卷 48-56页

作者： Cervantes, Jose-Antonio Lopez, Sonia Molina, Jahaziel Lopez, Francisco Perales-Tejeda, Monica Carmona-Frausto, Jesus Univ Guadalajara Dept Comp Sci & Engn Ameca 46600 Mexico Univ Guadalajara Lab Neuropsychol Ameca 46600 Mexico Inst Tecnol Ciudad Victoria Tecnol Nacl Mexico Div Grad Studies & Res Victoria City 87010 Mexico Univ Guadalajara Rd Guadalajara Ameca Km 45-5 Ameca 46600 Mexico

The need for engaging treatment approaches in mental health care has led to the developing of new applications based on serious game approaches. These approaches have facilitated the development of promising tools for dealing with attention deficit hyperactivity disorder. This paper presents a novel system called CogniDron-EEG. This system is based on a brain-computer interface for flying indoor a drone for cognitive training purposes. We conducted a controlled trial with ten healthy children aged 7-14 to test the functional suitability and usability of the CogniDron-EEG system. Also, this study allowed us to evaluate the preference between our system and another system based on video games. Therefore, participant subjects used our CogniDron-EEG system and a system called Nexus to identify the users' preferences concerning these two systems. The findings suggest that participants were satisfied with the CogniDron-EEG and provide the basis for further development and research on the CogniDron-EEG system. Therefore, the proposed system in this paper opens a new branch of research on drones to study their advantages and disadvantages of using them for cognitive training purposes. Additionally, implications for developing human-robot interaction and serious games in the mental health context are discussed.

关键词： brain-computer interface Unmanned aerial vehicles Neurofeedback Attention deficit-hyperactivity disorder

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The auditory P300-based single-switch brain-computer interface: Paradigm transition from healthy subjects to minimally conscious patients

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第2期59卷 81-90页

作者： Pokorny, Christoph Klobassa, Daniela S. Pichler, Gerald Erlbeck, Helena Real, Ruben G. L. Kuebler, Andrea Lesenfants, Damien Habbal, Dina Noirhomme, Quentin Risetti, Monica Mattia, Donatella Mueller-Putz, Gernot R. Graz Univ Technol Inst Knowledge Discovery A-8010 Graz Austria Albert Schweitzer Clin A-8020 Graz Austria Univ Wurzburg Dept Psychol D-97070 Wurzburg Germany Univ Liege Cyclotron Res Ctr B-4000 Liege Belgium Fdn Santa Lucia I-00179 Rome Italy

Objective: Within this work an auditory P300 brain-computer interface based on tone stream segregation, which allows for binary decisions, was developed and evaluated. Methods and materials: Two tone streams consisting of short beep tones with infrequently appearing deviant tones at random positions were used as stimuli. This paradigm was evaluated in 10 healthy subjects and applied to 12 patients in a minimally conscious state (MCS) at clinics in Graz, Wurzburg, Rome, and Liege. A stepwise linear discriminant analysis classifier with 10 x 10 cross-validation was used to detect the presence of any P300 and to investigate attentional modulation of the P300 amplitude. Results: The results for healthy subjects were promising and most classification results were better than random. In 8 of the 10 subjects, focused attention on at least one of the tone streams could be detected on a single-trial basis. By averaging 10 data segments, classification accuracies up to 90.6% could be reached. However, for MCS patients only a small number of classification results were above chance level and none of the results were sufficient for communication purposes. Nevertheless, signs of consciousness were detected in 9 of the 12 patients, not on a single-trial basis, but after averaging of all corresponding data segments and computing significant differences. These significant results, however, strongly varied across sessions and conditions. Conclusion: This work shows the transition of a paradigm from healthy subjects to MCS patients. Promising results with healthy subjects are, however, no guarantee of good results with patients. Therefore, more investigations are required before any definite conclusions about the usability of this paradigm for MCS patients can be drawn. Nevertheless, this paradigm might offer an opportunity to support bedside clinical assessment of MCS patients and eventually, to provide them with a means of communication. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Auditory P300 Tone stream segregation Clinical assessment Minimally conscious state brain-computer interface

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Mutual information-based optimization of sparse spatio-spectral filters in brain-computer interface

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期25卷 625-634页

作者： Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore ASTAR Inst Infocomm Res Singapore 138632 Singapore

Recently, neuro-rehabilitation based on brain-computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.

关键词： brain-computer interface EEG Mutual information Regularization Spatio-spectral filtering

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A Predictive Speller Controlled by a brain-computer interface Based on Motor Imagery

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ACM TRANSACTIONS ON computer-HUMAN INTERACTION 2012年第3期19卷 1–25页

作者： D'Albis, Tiziano Blatt, Rossella Tedesco, Roberto Sbattella, Licia Matteucci, Matteo Politecn Milan Dip Elect & Informaz I-20133 Milan Italy

Persons suffering from motor disorders have limited possibilities for communicating and normally require assistive technologies to fulfill this primary need. Promising means of providing basic communication abilities to subjects affected by severe motor impairments include brain-computer interfaces (BCIs), that is, systems that directly translate brain signals into device commands, bypassing any muscle or nerve mediation. To date, the use of BCIs for effective verbal communication is yet an open issue, primarily due to the low rates of information transfer that can be achieved with this technology. Still, performance of BCI spelling applications could be considerably improved by a smart user interface design and by the adoption of natural language processing (NLP) techniques for text prediction. The objective of this work is to suggest an approach and a user interface for BCI spelling applications combining state-of-the-art BCI and NLP techniques to maximize the overall communication rate of the system. The BCI paradigm adopted is motor imagery, that is, when the subject imagines moving a certain part of the body, he/she produces modifications to specific brain rhythms that are detected in real-time through an electroencephalogram and translated into commands for a spelling application. By maximizing the overall communication rate, our approach is twofold: on one hand, we maximize the information transfer rate from the control signal, on the other hand, we optimize the way this information is employed for the purpose of verbal communication. The achieved results are satisfactory and comparable with the latest works reported in literature on motor-imagery BCI spellers. For the three subjects tested, we obtained a spelling rate of respectively 3 char/min, 2.7 char/min, and 2 char/min.

关键词： BMI BCI NLP speller brain-computer interface text prediction

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Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface

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JOURNAL OF NEURAL ENGINEERING 2021年第6期18卷

作者： Chen, Lingling Chen, Pengfei Zhao, Shaokai Luo, Zhiguo Chen, Wei Pei, Yu Zhao, Hongyu Jiang, Jing Xu, Minpeng Yan, Ye Yin, Erwei Hebei Univ Technol Sch Artificial Intelligence & Data Sci Tianjin 300130 Peoples R China Engn Res Ctr Intelligent Rehabil Device & Detect Minist Educ Tianjin 300130 Peoples R China Acad Mil Sci AMS Def Innovat Inst Beijing 100071 Peoples R China Tianjin Artificial Intelligence Innovat Ctr TAIIC Tianjin 300450 Peoples R China Tianjin Univ Tianjin 300072 Peoples R China China Astronaut Res & Training Ctr Natl Key Lab Human Factors Engn Beijing 100094 Peoples R China East China Univ Sci & Technol Shanghai 200237 Peoples R China Natl Res Ctr Rehabil Tech Aids Beijing 100176 Peoples R China

Objective. brain-controlled robotic arms have shown broad application prospects with the development of robotics, science and information decoding. However, disadvantages, such as poor flexibility restrict its wide application. Approach. In order to alleviate these drawbacks, this study proposed a robotic arm asynchronous control system based on steady-state visual evoked potential (SSVEP) in an augmented reality (AR) environment. In the AR environment, the participants were able to concurrently see the robot arm and visual stimulation interface through the AR device. Therefore, there was no need to switch attention frequently between the visual stimulation interface and the robotic arm. This study proposed a multi-template algorithm based on canonical correlation analysis and task-related component analysis to identify 12 targets. An optimization strategy based on dynamic window was adopted to adjust the duration of visual stimulation adaptively. Main results. Experimental results of this study found that the high-frequency SSVEP-based brain-computer interface (BCI) realized the switch of the system state, which controlled the robotic arm asynchronously. The average accuracy of the offline experiment was 94.97%, whereas the average information translate rate was 67.37 +/- 14.27 ***(-1). The online results from ten healthy subjects showed that the average selection time of a single online command was 2.04 s, which effectively reduced the visual fatigue of the subjects. Each subject could quickly complete the puzzle task. Significance. The experimental results demonstrated the feasibility and potential of this human-computer interaction strategy and provided new ideas for BCI-controlled robots.

关键词： brain-computer interface robotic arm augmented reality dynamic window asynchronous control

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Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2021年第5期59卷 1133-1150页

作者： Carvalho, Sarah Negreiros de Vargas, Guilherme Vettorazzi da Silva Costa, Thiago Bulhoes de Arruda Leite, Harlei Miguel Coradine, Luis Boccato, Levy Soriano, Diogo Coutinho Attux, Romis Univ Fed Ouro Preto Inst Exact & Appl Sci Ouro Preto Brazil BRAINN Brazilian Inst Neurosci & Neurotechnol Campinas Brazil Univ Estadual Campinas UNICAMP Sch Comp & Elect Engn Campinas Brazil Univ Fed Alagoas Inst Comp UFAL Maceio Alagoas Brazil Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Santo Andre SP Brazil

brain-computer interfaces (BCI) based on steady-state visually evoked potentials (SSVEP) have been increasingly used in different applications, ranging from entertainment to rehabilitation. Filtering techniques are crucial to detect the SSVEP response since they can increase the accuracy of the system. Here, we present an analysis of a space-time filter based on the Minimum Variance Distortionless Response (MVDR). We have compared the performance of a BCI-SSVEP using the MVDR filter to other classical approaches: Common Average Reference (CAR) and Canonical Correlation Analysis (CCA). Moreover, we combined the CAR and MVDR techniques, totalling four filtering scenarios. Feature extraction was performed using Welch periodogram, Fast Fourier transform, and CCA (as extractor) with one and two harmonics. Feature selection was performed by forward wrappers, and a linear classifier was employed for discrimination. The main analyses were carried out over a database of ten volunteers, considering two cases: four and six visual stimuli. The results show that the BCI-SSVEP using the MVDR filter achieves the best performance among the analysed scenarios. Interestingly, the system's accuracy using the MVDR filter is practically constant even when the number of visual stimuli was increased, whereas degradation was observed for the other techniques.

关键词： brain-computer interface Steady-state visually evoked potential Spatial filtering Temporal filtering Minimum variance distortionless response

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Comparison of feature selection and classification methods for a brain-computer interface driven by non-motor imagery

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2010年第2期48卷 123-132页

作者： Cabrera, Alvaro Fuentes Farina, Dario Dremstrup, Kim Aalborg Univ Dept Hlth Sci & Technol Ctr Sensory Motor Interact DK-9220 Aalborg Denmark

The aim of this study was to compare methods for feature extraction and classification of EEG signals for a brain-computer interface (BCI) driven by auditory and spatial navigation imagery. Features were extracted using autoregressive modeling and optimized discrete wavelet transform. The features were selected with exhaustive search, from the combination of features of two and three channels, and with a discriminative measure (r (2)). Moreover, Bayesian classifier and support vector machine (SVM) with Gaussian kernel were compared. The results showed that the two classifiers provided similar classification accuracy. Conversely, the exhaustive search of the optimal combination of features from two and three channels significantly improved performance with respect to using r (2) for channel selection. With features optimally extracted from three channels with optimized scaling filter in the discrete wavelet transform, the classification accuracy was on average 72.2%. Thus, the choice of features had greater impact on performance than the choice of the classifier for discrimination between the two non-motor imagery tasks investigated. The results are relevant for the choice of the translation algorithm for an on-line BCI system based on non-motor imagery.

关键词： brain-computer interface Auditory imagery Spatial navigation imagery Autoregressive modeling Wavelets r(2) Bayesian classifier Support vector machine

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Pleasure detection by online brain-computer interface

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MEASUREMENT 2011年第1期44卷 121-128页

作者： Mohy-Ud-Din, Zia Woo, Sang Hyo Lee, Jung Hyun Lee, Maan Gee Kim, Jee Hyun Cho, Jin Ho Kyungpook Natl Univ Sch Med Dept Med & Biol Engn Taegu South Korea Kyungpook Natl Univ Sch Elect Engn & Comp Sci Taegu South Korea Kyungpook Natl Univ Sch Med Dept Pharmacol Taegu South Korea

This paper seeks to elucidate the design and implementation of an instrumentation amplifier, filters, LabVIEW-based spike detection, and automatic spike counting to detect pleasure sensation in the rat using invasive BCI. This method involved sites related to pleasure, and after acquiring signals from the ventral pallidum, facial motor cortex, and orbitofrontal cortex, these signals were analyzed to assess the pleasure sensation in the rat. The results illustrated a decreased spike rate in the motor and orbitofrontal cortices and an increased spike rate in the ventral pallidum during pleasure. The pleasure detection experiment was conducted four times to obtain the mean values of spike rates. The motor cortex had 9 spikes/s, the orbitofrontal cortex had 18 spikes/s and the ventral pallidum had 34 spikes/s. The correlation coefficient is above 78%, effectiveness of the experiment. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Pleasure detection Neural amplifier Neural spikes brain-computer interface

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