RESUMEN
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency is highly dependent on individual training data acquired during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we introduce SSVEP-DAN, the first dedicated neural network model designed to align SSVEP data across different domains, encompassing various sessions, subjects, or devices. Our experimental results demonstrate the ability of SSVEP-DAN to transform existing source SSVEP data into supplementary calibration data. This results in a significant improvement in SSVEP decoding accuracy while reducing the calibration time. We envision SSVEP-DAN playing a crucial role in future applications of high-performance SSVEP-based BCIs. The source code for this work is available at: https://github.com/CECNL/SSVEP-DAN.
Asunto(s)
Algoritmos , Interfaces Cerebro-Computador , Electroencefalografía , Potenciales Evocados Visuales , Humanos , Potenciales Evocados Visuales/fisiología , Masculino , Adulto , Femenino , Redes Neurales de la Computación , Adulto Joven , Calibración , Reproducibilidad de los ResultadosRESUMEN
Objective. The electroencephalogram (EEG) is gaining popularity as a physiological measure for neuroergonomics in human factor studies because it is objective, less prone to bias, and capable of assessing the dynamics of cognitive states. This study investigated the associations between memory workload and EEG during participants' typical office tasks on a single-monitor and dual-monitor arrangement. We expect a higher memory workload for the single-monitor arrangement.Approach. We designed an experiment that mimics the scenario of a subject performing some office work and examined whether the subjects experienced various levels of memory workload in two different office setups: (1) a single-monitor setup and (2) a dual-monitor setup. We used EEG band power, mutual information, and coherence as features to train machine learning models to classify high versus low memory workload states.Main results. The study results showed that these characteristics exhibited significant differences that were consistent across all participants. We also verified the robustness and consistency of these EEG signatures in a different data set collected during a Sternberg task in a prior study.Significance. The study found the EEG correlates of memory workload across individuals, demonstrating the effectiveness of using EEG analysis in conducting real-world neuroergonomic studies.
Asunto(s)
Electroencefalografía , Carga de Trabajo , Humanos , Carga de Trabajo/psicología , Electroencefalografía/métodos , Memoria , Aprendizaje AutomáticoRESUMEN
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 electroencephalogram montages).Main results. Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method.Significance. This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.
Asunto(s)
Interfaces Cerebro-Computador , Potenciales Evocados Visuales , Algoritmos , Electroencefalografía/métodos , Humanos , Aprendizaje Automático , Estimulación LuminosaRESUMEN
Task-related component analysis (TRCA) has been the most effective spatial filtering method in implementing high-speed brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs). TRCA is a data-driven method, in which spatial filters are optimized to maximize inter-trial covariance of time-locked electroencephalographic (EEG) data, formulated as a generalized eigenvalue problem. Although multiple eigenvectors can be obtained by TRCA, the traditional TRCA-based SSVEP detection considered only one that corresponds to the largest eigenvalue to reduce its computational cost. This study proposes using multiple eigen-vectors to classify SSVEPs. Specifically, this study integrates a task consistency test, which statistically identifies whether the component reconstructed by each eigenvector is task-related or not, with the TRCA-based SSVEP detection method. The proposed method was evaluated by using a 12-class SSVEP dataset recorded from 10 subjects. The study results indicated that the task consistency test usually identified and suggested more than one eigenvectors (i.e., spatial filters). Further, the use of additional spatial filters significantly improved the classification accuracy of the TRCA-based SSVEP detection.
Asunto(s)
Interfaces Cerebro-Computador , Potenciales Evocados Visuales , Algoritmos , Electroencefalografía , Humanos , Examen NeurológicoRESUMEN
This commentary presents a replication study to verify the effectiveness of a sum of squared correlations (SSCOR)-based steady-state visual evoked potentials (SSVEPs) decoding method proposed by Kumar et al.. We implemented the SSCOR-based method in accordance with their descriptions and estimated its classification accuracy using a benchmark SSVEP dataset with cross validation. Our results showed significantly lower classification accuracy compared with the ones reported in Kumar et al.'s study. We further investigated the sources of performance discrepancy by simulating data leakage between training and test datasets. The classification performance of the simulation was remarkably similar to those reported by Kumar et al.. We, therefore, question the validity of evaluation and conclusions drawn in Kumar et al.'s study.
Asunto(s)
Interfaces Cerebro-Computador , Potenciales Evocados Visuales , Benchmarking , Electroencefalografía , Examen NeurológicoRESUMEN
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.
Asunto(s)
Interfaces Cerebro-Computador , Potenciales Evocados Visuales , Algoritmos , Calibración , Electroencefalografía , Humanos , Estimulación LuminosaRESUMEN
Our previous study has demonstrated the feasibility of employing non-hair-bearing electrodes to build a Steadystate Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, relaxing technical barriers in preparation time and offering an ease-of-use apparatus. The signal quality of the SSVEPs and the resultant performance of the non-hair BCI, however, did not close upon those reported in the state-of-the-art BCI studies based on the electroencephalogram (EEG) measured from the occipital regions. Recently, advanced decoding algorithms such as task-related component analysis have made a breakthrough in enhancing the signal quality of the occipital SSVEPs and the performance of SSVEP-based BCIs in a well-controlled laboratory environment. However, it remains unclear if the advanced decoding algorithms can extract highfidelity SSVEPs from the non-hair EEG and enhance the practicality of non-hair BCIs in real-world environments. This study aims to quantitatively evaluate whether, and if so, to what extent the non-hair BCIs can leverage the state-of-art decoding algorithms. Eleven healthy individuals participated in a 5-target SSVEP BCI experiment. A high-density EEG cap recorded SSVEPs from both hair-covered and non-hair-bearing regions. By evaluating and demonstrating the accessibility of nonhair-bearing behind-ear signals, our assessment characterized constraints on data length, trial numbers, channels, and their relationships with the decoding algorithms, providing practical guidelines to optimize SSVEP-based BCI systems in real-life applications.