Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyo...Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.展开更多
A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SN...A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SNR). In this paper, a novel method is proposed to cope with this problem through sparse representation for the P300 speller paradigm. This work is distinguished using two key contributions. First, we investigate sparse coding and its feasibility for brain signal classification. Training signals are used to learn the dictionaries and test signals are classified according to their sparse representation and reconstruction errors. Second, sample selection and a channel-aware dictionary are proposed to reduce the effect of noise, which can improve performance and enhance the computing efficiency simultaneously. A novel classification method from the sample set perspective is proposed to exploit channel correlations. Specifically, the brain signal of each channel is classified jointly using its spatially neighboring channels and a novel weighted regulation strategy is proposed to overcome outliers in the group. Experimental results have demonstrated that our methods are highly effective. We achieve a state-of-the-art recognition rate of 72.5%, 88.5%, and 98.5% at 5, 10, and 15 epochs, respectively, on BCI Competition Ⅲ Dataset Ⅱ.展开更多
The P300 event-related potential (ERP), with advantages of high stability and no need for initial training, is one of the most commonly used responses in brain-computer interface (BCI) applications. The row/column par...The P300 event-related potential (ERP), with advantages of high stability and no need for initial training, is one of the most commonly used responses in brain-computer interface (BCI) applications. The row/column paradigm (RCP) that flashes an entire column or row of a visual matrix has been used successfully to help patients to spell words. However, RCP remains subject to errors that slow down communication, such as adjacency-distraction and double-flash errors. In this paper, a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed. SBP divides a 6×6 matrix into several submatrices. Each submatrix flashes in single cell paradigm (SCP) mode and separately performs an ensemble averaging method according to the sequences. The parameter of sequence number is used to improve further the accuracy and information transfer rate (ITR). SBP has advantages of flexibility in division of the matrix and better expansion capability, which were confirmed with different divisions of the 6×6 matrix and expansion to a 6×9 matrix. Stimulation results show that SBP is superior to RCP in performance and user acceptability.展开更多
基金This article contains results and findings from a research project that was supported by King Abdulaziz City for Science and Technology,http://www.kacst.edu.sa/,Grant No.827-37-11。
文摘Technology has tremendously contributed to improving communication and facilitating daily activities.Brain-Computer Interface(BCI)study particularly emerged from the need to serve people with disabilities such as Amyotrophic Lateral Sclerosis(ALS).However,with the advancements in cost-effective electronics and computer interface equipment,the BCI study is flourishing,and the exploration of BCI applications for people without disabilities,to enhance normal functioning,is increasing.Particularly,the P300-based spellers are among the most promising applications of the BCI technology.In this context,the region-based paradigm for P300 BCI spellers was introduced in an effort to reduce the crowding effect and adjacency problem that might affect the detection of P300 peak.This study extends this line of research by investigating the effect,in terms of accuracy and usability,of the letters’distribution among the speller’s regions.For this purpose,a clustering algorithm is proposed,and two region-based layouts were generated by redistributing the letters based on their dissimilarity or their similarity.A pilot usability evaluation was also conducted in order to assess the usability of the different layouts in terms of effectiveness,efficiency,and satisfaction.The results indicate that the distribution of the letters has an effect on the classification accuracy as well as the user experience.Particularly,when considering short-term accuracy and cognitive effort,the original region-based layout outperforms other layouts.
基金supported by the National High Technology Research and Development (863) Program of China(No. 2012AA011004)the National Science and Technology Support Program (No. 2013BAK02B04)。
文摘A Brain-Computer Interface(BCI) aims to produce a new way for people to communicate with computers.Brain signal classification is a challenging issue owing to the high-dimensional data and low Signal-to-Noise Ratio(SNR). In this paper, a novel method is proposed to cope with this problem through sparse representation for the P300 speller paradigm. This work is distinguished using two key contributions. First, we investigate sparse coding and its feasibility for brain signal classification. Training signals are used to learn the dictionaries and test signals are classified according to their sparse representation and reconstruction errors. Second, sample selection and a channel-aware dictionary are proposed to reduce the effect of noise, which can improve performance and enhance the computing efficiency simultaneously. A novel classification method from the sample set perspective is proposed to exploit channel correlations. Specifically, the brain signal of each channel is classified jointly using its spatially neighboring channels and a novel weighted regulation strategy is proposed to overcome outliers in the group. Experimental results have demonstrated that our methods are highly effective. We achieve a state-of-the-art recognition rate of 72.5%, 88.5%, and 98.5% at 5, 10, and 15 epochs, respectively, on BCI Competition Ⅲ Dataset Ⅱ.
基金Project (No. 61071062) supported by the National Natural Science Foundation of China
文摘The P300 event-related potential (ERP), with advantages of high stability and no need for initial training, is one of the most commonly used responses in brain-computer interface (BCI) applications. The row/column paradigm (RCP) that flashes an entire column or row of a visual matrix has been used successfully to help patients to spell words. However, RCP remains subject to errors that slow down communication, such as adjacency-distraction and double-flash errors. In this paper, a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed. SBP divides a 6×6 matrix into several submatrices. Each submatrix flashes in single cell paradigm (SCP) mode and separately performs an ensemble averaging method according to the sequences. The parameter of sequence number is used to improve further the accuracy and information transfer rate (ITR). SBP has advantages of flexibility in division of the matrix and better expansion capability, which were confirmed with different divisions of the 6×6 matrix and expansion to a 6×9 matrix. Stimulation results show that SBP is superior to RCP in performance and user acceptability.
