Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in ...Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness （DOCs）. Brain- computer interface （BCI） can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expres- sion. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.展开更多
Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most ...Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.展开更多
Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines an...Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.展开更多
A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detectio...A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.展开更多
An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for fre...An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.展开更多
In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheel...In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.展开更多
The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSV...The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.展开更多
Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this st...Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies.Then,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials.Afterward,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate.Finally,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.展开更多
Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)tec...Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.展开更多
This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication....This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication.The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient,achieving a maximum free-spelling accuracy above 90%and an information transfer rate of over 22.203 bits/min.A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design.The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.展开更多
A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype para...A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between high level commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory;research challenges, including human factors in navigation application interaction design;and the feasibility of hybrid BCI for BCI navigation are discussed in detail.展开更多
Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plastic...Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.展开更多
Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-...Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97%can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.展开更多
基金supported by the National Key Research and Development Program of China (2017YFB1002505)the National Natural Science Foundation of China (61633010, 91420302, and 61503143)+1 种基金the Natural Science Foundation of Guangdong Province, China (2014A030312005 and 2014A030310244)the Pearl River S&T Nova Program of Guangzhou Municipality, China (201710010038)
文摘Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness （DOCs）. Brain- computer interface （BCI） can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expres- sion. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R.
文摘Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.
基金National Natural Science Foundation of China(No.51005176)Research Fund for the Doctoral Program of Higher Education of China(No.20100201120003)
文摘Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.
基金National Natural Science Foundation of China(90820305,60775040)
文摘A two-stage state recognition method is proposed for asynchronous SSVEP(steady-state visual evoked potential) based brain-computer interface(SBCI) system.The two-stage method is composed of the idle state(IS) detection and control state(CS) discrimination modules.Based on blind source separation and continuous wavelet transform techniques,the proposed method integrates functions of multi-electrode spatial filtering and feature extraction.In IS detection module,a method using the ensemble IS feature is proposed.In CS discrimination module,the ensemble CS feature is designed as feature vector for control intent classification.Further,performance comparisons are investigated among our IS detection module and other existing ones.Also the experimental results validate the satisfactory performance of our CS discrimination module.
基金the National Natural Science Foundation of China(Nos.61702395 and 61972302)the Science and Technology Projects of Xi’an,China(No.201809170CX11JC12)。
文摘An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.
文摘In recent years, Brain Computer Interface (BCI) systems based on Steady-State Visual Evoked Potential (SSVEP) have received much attention. This study tries to develop a SSVEP based BCI system that can control a wheelchair prototype in five different positions including stop position. In this study four different flickering frequencies in low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using Lab-VIEW. Four stimuli colors, green, red, blue and violet were used to investigate the color influence in SSVEPs. The Electroencephalogram (EEG) signals recorded from the occipital region were segmented into 1 second window and features were extracted by using Fast Fourier Transform (FFT). One-Against-All (OAA), a popular strategy for multiclass SVM, is used to classify SSVEP signals. During stimuli color comparison SSVEP with violet color showed higher accuracy than that with green, red and blue stimuli.
基金supported by the National Natural Science Foundation of China(Grant Nos.U20A20191,61727807,82071912,12104049)the Beijing Municipal Science&Technology Commission(Grant No.Z201100007720009)+4 种基金the Fundamental Research Funds for the Central Universities(Grant No.2021CX11011)the China Postdoctoral Science Foundation(Grant No.2020TQ0040)the National Key Research and Development Program of China(Grant No.2020YFC2007305)the BIT Research and Innovation Promoting Project(Grant No.2022YCXZ026)the Ensan Foundation(Grant No.2022026)。
文摘The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.
文摘Brain–computer interface is a new form of interaction between humans and machines.This interaction helps the human brain control or operate external devices directly using electroencephalograph(EEG)signals.In this study,we first adopt a canonical correlation analysis method to find the stimulation frequency by calculating the correlation coefficient between the EEG data and multiple sets of harmonics with different frequencies.Then,we select the maximum correlation coefficient as the stimulus frequency and consequently identify steady-state visual evoked potentials.Afterward,we introduce power spectral density to adjust the stimulus frequency and a voting mechanism to reduce the false activation rate.Finally,we build a virtual household electrical appliance brain–computer control interface,which achieves over 72.84%accuracy for three classification problems.
基金Supported by Innovative Talents Training Project in the Basic Educational Stage of Beijing(“Soaring Program”Instrument and Student Training in Aerospace Field,Under No.631306)。
文摘Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.
基金supported by the Key Clinical Projects of Peking University Third Hospital(No.Y76437-01)National Key Research and Development Program of China(No.2017YFB1002505)+2 种基金National Natural Science Foundation of China under Grant(No.61431007)Key Research and Development Program of Guangdong Province(No.2018B030339001)Doctoral Brain+X Seed Grand Program of Tsinghua University.
文摘This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication.The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient,achieving a maximum free-spelling accuracy above 90%and an information transfer rate of over 22.203 bits/min.A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design.The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.
基金Supported by Key-Area Research and Development Program of Guangdong Province (2019B010149001)the National NaturalScience Foundation of China (61960206007)the 111 Project (B18005)
文摘A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between high level commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory;research challenges, including human factors in navigation application interaction design;and the feasibility of hybrid BCI for BCI navigation are discussed in detail.
基金supported by the National Natural Science Foundation of China (U19B2030, 61976167, 62301405, 62101416)the Natural Science Basic Research Program of Shaanxi, China (2022JQ-708)Fundamental Research Funds for the Central Universities, China.
文摘Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
基金supported by the National Natural Science Foundation of China (Grant Nos.30370393,30640040)the NSF of Hubei Province (No.2007ABA098).
文摘Communication signals should be estimated by a single trial in a brain-computer interface.Since the relativity of visual evoked potentials from different sites should be stronger than those of the spontaneous electro-encephalogram(EEG),this paper adopted the time-lock averaged signals from multi-channels as features.200 trials of EEG recordings evoked by target or non-target stimuli were classified by the support vector machine(SVM).Results show that a classification accuracy of higher than 97%can be obtained by merely using the 250–550 ms time section of the averaged signals with channel Cz and Pz as features.It suggests that a possible approach to boost communication speed and simplify the designation of the brain-computer interface(BCI)system is worthy of an attempt in this way.
