Filter bank extensions for subject non-specific SSVEP based BCIs
01 Mar 2019-pp 627-630
TL;DR: The results demonstrate that the proposed two stage (a filter bank stage followed by SSVEP detection) implementation of popular multichannel algorithms provide significant improvement in performance at short datalengths of < 2.75 s and can be viewed as a potential standard detection approach across allSSVEP identification problems.
Abstract: Recently, filter bank analysis has been used in several detection methods to extract selective frequency features across multiple brain computer interface (BCI) modalities due to its effectiveness and simple structure. In this work, we propose filter bank technique as a standard preprocessing method for popular training free multi-channel steady-state visual evoked potential (SSVEP) detection methods to overcome subject-specific performance differences and a general improvement in detection accuracy. Our study validates the effectiveness of filter bank extensions by comparing performance differences of multichannel methods with their filter bank counterparts using a forty target SSVEP benchmark dataset collected across thirty five subjects. The results demonstrate that the proposed two stage (a filter bank stage followed by SSVEP detection) implementation of popular multichannel algorithms provide significant improvement in performance at short datalengths of < 2.75 s (p < 0.001) and can be viewed as a potential standard detection approach across all SSVEP identification problems.
Citations
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TL;DR: This study proposed a new training-free dynamical optimization algorithm, which significantly improved the performance of online SSVEP-based BCI systems and significantly outperforms STE-DW and FBCCA-FW in terms of accuracy and ITR.
Abstract: Objective: Filter bank canonical correlation analysis (FBCCA) is a widely-used classification approach implemented in steady-state visual evoked potential (SSVEP)–based brain-computer interfaces (BCIs). However, conventional detection algorithms for SSVEP recognition problems, including the FBCCA, were usually based on 'fixed window' strategy. That's to say, these algorithms always analyze data with fixed length. This study devoted to enhance the performance of SSVEP-based BCIs by designing a new dynamic window strategy which automatically finds an optimal data length to achieve higher information transfer rate (ITR). Approach: The main purpose of 'dynamic window' is to minimize the required data length while maintaining high accuracy. This study projected the correlation coefficients of FBCCA into probability space by softmax function and built a hypothesis testing model, which took risk function as evaluation of classification result's 'credibility'. In order to evaluate the superiority of this approach, FBCCA with fixed data length (FBCCA-FW) and spatial temporal equalization dynamic window (STE-DW) were implemented for comparison. Main results: Fourteen healthy subjects' results were concluded by a 40-target online SSVEP-based BCI speller system. The results suggest that this proposed approach significantly outperforms STE-DW and FBCCA-FW in terms of accuracy and ITR. Significance: By incorporating the fundamental ideas of FBCCA and dynamic window strategy, this study proposed a new training-free dynamical optimization algorithm, which significantly improved the performance of online SSVEP-based BCI systems.
53 citations
TL;DR: A novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems by leveraging user-specific data recorded in previous sessions.
Abstract: Objective : This paper proposes a novel device-to-device transfer-learning algorithm for reducing the calibration cost in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) speller by leveraging electroencephalographic (EEG) data previously acquired by different EEG systems. Methods : The transferring is done by projecting the scalp-channel EEG signals onto a shared latent domain across devices. Three spatial filtering techniques, including channel averaging, canonical correlation analysis (CCA), and task-related component analysis (TRCA), were employed to extract the shared responses from different devices. The transferred data were integrated into a template-matching-based algorithm to detect SSVEPs. To evaluate its transferability, this paper conducted two sessions of simulated online BCI experiments with ten subjects using 40 visual stimuli modulated by joint frequency-phase coding method. In each session, two different EEG devices were used: first, the Quick-30 system (Cognionics, Inc.) with dry electrodes, and second, the ActiveTwo system (BioSemi, Inc.) with wet electrodes. Results : The proposed method with CCA- and TRCA-based spatial filters achieved significantly higher classification accuracy compared with the calibration-free standard CCA-based method. Conclusion : This paper validated the feasibility and effectiveness of the proposed method in implementing calibration-free SSVEP-based BCIs. Significance : The proposed method has great potentials to enhance practicability and usability of real-world SSVEP-based BCI applications by leveraging user-specific data recorded in previous sessions even with different EEG systems and montages.
36 citations
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01 Jul 2020
TL;DR: The proposed method, subject-calibration extended FBCCA (SCEF) leverages independent and distinct discrimination characteristics of multiple references with subject-specific weight-adjusted features to improve SSVEP recognition performance.
Abstract: Steady-State Visual Evoked Potential (SSVEP) BCI brings high accuracy and consistent performance across subjects at the expense of a long stimulus presentation time window. Several recent methods exploited subject-specific features to improve SSVEP recognition performance in a short time window less than 1s. Although the calibration process is tedious and causes inconvenience, small calibration data with short duration resulting in higher performance gains are worth considering. So we propose a method by optimizing Filter-Bank Canonical Correlation Analysis (FBCCA) with subjects’ calibrated templates, subject-specific weights and multiple reference types. The proposed method, subject-calibration extended FBCCA (SCEF) leverages independent and distinct discrimination characteristics of multiple references with subject-specific weight-adjusted features to improve SSVEP recognition performance. We tested the proposed method with different parameters compared with FBCCA baseline and state-of-the-art calibration methods on forty targets SSVEP dataset using 0.2s to 4s time windows. Our evaluation results show SCEF with three reference templates and subject-specific weighted features perform significantly better than all FBCCA variants in 0.2 s to 1 s time window (p < 0.001). SCEF performs marginally, not statistically significant, better than existing methods about 2.69 ± 2.32% mean accuracy across time windows. Including multiple templates and subject-specific weight increases 15.73 ± 5.34% and 8.06± 2.06% in mean accuracy resulting the overall performance improvements in short time window. The proposed optimization only requires prior calibration data to create subject-specific templates and weights instead of learning features from calibration data every time. This enables not requiring to repeat the calibration step in every SSVEP session for the same subject while still maintaining accuracy similar to state-of-the-art calibration methods.
2 citations
TL;DR: In this paper , a deep convolutional neural network (CNN) was proposed for classifying a 40-class SSVEP, which achieved 88.5% average accuracy for the nine-channel EEG with the mean and max ITR of 72 and 91.23 bpm.
Abstract: The main drawback of SSVEP-based BCIs is the lack of a classifier that categorizes SSVEPs with high accuracy and Information Transfer Rate (ITR). Addressing this, we proposed a deep convolutional neural network (CNN) for classifying a 40-class SSVEP. Time windows of length 2 and 3.5 seconds were used for training and testing the model by leave-one-subject-out cross-validation, using nine, three, and single-channel EEG. The proposed model reached 88.5% average accuracy for the nine-channel EEG with the mean and max ITR of 72 and 91.23 bpm, respectively. It outperformed the previous deep learning methods for SSVEP-based BCIs, in terms of accuracy and ITR. In the three-channel experiment the mean accuracy and ITR were 76.02% and 40.1 bpm. In single-channel implementation, O1 channel achieved 77.38 % average accuracy (highest) and the mean ITR was 57.51 bpm. The model showed promising performance to put this technology forward and make it more practical.