ArticleSensors (Basel, Switzerland)2025
Dual-Mode Visual System for Brain-Computer Interfaces: Integrating SSVEP and P300 Responses.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Improved SSVEP Classification Through EEG Artifact Reduction Using Auxiliary Sensors.Sensors (Basel, Switzerland) · 2026Article
- Applications of Endovascular Brain-Computer Interface in Patients with Alzheimer's Disease.Research (Washington, D.C.) · 2025Review
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Authors and funding
2 authors.
Funding
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Abstract
In brain-computer interface (BCI) systems, steady-state visual-evoked potentials (SSVEP) and P300 responses have achieved widespread implementation owing to their superior information transfer rates (ITR) and minimal training requirements. These neurophysiological signals have exhibited robust efficacy and versatility in external device control, demonstrating enhanced precision and scalability. However, conventional implementations predominantly utilise liquid crystal display (LCD)-based visual stimulation paradigms, which present limitations in practical deployment scenarios. This investigation presents the development and evaluation of a novel light-emitting diode (LED)-based dual stimulation apparatus designed to enhance SSVEP classification accuracy through the integration of both SSVEP and P300 paradigms. The system employs four distinct frequencies-7 Hz, 8 Hz, 9 Hz, and 10 Hz-corresponding to forward, backward, right, and left directional controls, respectively. Oscilloscopic verification confirmed the precision of these stimulation frequencies. Real-time feature extraction was accomplished through the concurrent analysis of maximum Fast Fourier Transform (FFT) amplitude and P300 peak detection to ascertain user intent. Directional control was determined by the frequency exhibiting maximal amplitude characteristics. The visual stimulation hardware demonstrated minimal frequency deviation, with error differentials ranging from 0.15% to 0.20% across all frequencies. The implemented signal processing algorithm successfully discriminated between all four stimulus frequencies whilst correlating them with their respective P300 event markers. Classification accuracy was evaluated based on correct task intention recognition. The proposed hybrid system achieved a mean classification accuracy of 86.25%, coupled with an average ITR of 42.08 bits per minute (bpm). These performance metrics notably exceed the conventional 70% accuracy threshold typically employed in BCI system evaluation protocols.
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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.