ArticleSensors (Basel, Switzerland)2022
An Adaptive Task-Related Component Analysis Method for SSVEP Recognition.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.Sensors (Basel, Switzerland) · 2026Article
- A novel hybrid method based on task-related component and canonical correlation analyses (H-TRCCA) for enhancing SSVEP recognition.Frontiers in neuroscience · 2025Article
- Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.Sensors (Basel, Switzerland) · 2023Article
- A Sparse Representation Classification Scheme for the Recognition of Affective and Cognitive Brain Processes in Neuromarketing.Sensors (Basel, Switzerland) · 2023Article
- Human Recognition Using Deep Neural Networks and Spatial Patterns of SSVEP Signals.Sensors (Basel, Switzerland) · 2023Article
- SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Steady-State Visual Evoked Potential (SSVEP) recognition methods use a subject's calibration data to differentiate between brain responses, hence, providing the SSVEP-based brain-computer interfaces (BCIs) with high performance. However, they require sufficient calibration EEG trials to achieve that. This study develops a new method to learn from limited calibration EEG trials, and it proposes and evaluates a novel adaptive data-driven spatial filtering approach for enhancing SSVEP detection. The spatial filter learned from each stimulus utilizes temporal information from the corresponding EEG trials. To introduce the temporal information into the overall procedure, a multitask learning approach, based on the Bayesian framework, is adopted. The performance of the proposed method was evaluated into two publicly available benchmark datasets, and the results demonstrated that our method outperformed competing methods by a significant margin.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.