Objective: The P300 speller is intended to restore communication to patients with advanced neuromuscular disorders, but clinical implementation may be hindered by several factors, including system setup, burden, and cost. Our goal was to develop a method that can overcome these barriers by optimizing EEG electrode number and placement for P300 studies within a population of subjects.
Methods: A Gibbs sampling method was developed to find the optimal electrode configuration given a set of P300 speller data. The method was tested on a set of data from 15 healthy subjects using an established 32-electrode pattern. Resulting electrode configurations were then validated using online prospective testing with a naïve Bayes classifier in 15 additional healthy subjects.
Results: The method yielded a set of four posterior electrodes (PO₈, PO₇, POZ, CPZ), which produced results that are likely sufficient to be clinically effective. In online prospective validation testing, no significant difference was found between subjects' performances using the reduced and the full electrode configurations.
Conclusions: The proposed method can find reduced sets of electrodes within a subject population without reducing performance.
Significance: Reducing the number of channels may reduce costs, set-up time, signal bandwidth, and computation requirements for practical online P300 speller implementation.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4377128 | PMC |
| http://dx.doi.org/10.1016/j.clinph.2014.09.021 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-related Potentials.
IEEE Trans Biomed Eng
November 2024
Objective: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.
Methods: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF).
Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).
J Neural Eng
October 2024
Electrical and Computer Engineering, University of Houston, N308 Engineering Building I, Houston, Texas, 77204-4005, UNITED STATES.
Article Synopsis
- * This research investigates the use of non-invasive EEG to develop speech Brain-Computer Interfaces (BCIs) that decode speech features directly, aiming for a more natural communication method.
- * Deep learning models, such as CNNs and RNNs, were tested for speech decoding tasks, showing significant success in distinguishing both discrete and continuous speech elements, while also indicating the importance of specific EEG frequency bands for performance.
The calibration procedure for a wearable P300 brain-computer interface (BCI) greatly impact the user experience of the system. Each user needs to spend additional time establishing a decoder adapted to their own brainwaves. Therefore, achieving subject independent is an urgent issue for wearable P300 BCI needs to be addressed.
View Article and Find Full Text PDFAn Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks.
Brain Sci
August 2024
Centro de Investigación en Informática Aplicada (CIDIA), Universidad Nacional de Hurlingham (UNAHUR), Hurlingham B1688, Argentina.
This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision.
View Article and Find Full Text PDFA Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection.
IEEE Trans Neural Syst Rehabil Eng
June 2024
Visual-based brain-computer interface (BCI) enables people to communicate with others by spelling words from the brain and helps professionals recognize targets in large numbers of images. P300 signals evoked by different types of stimuli, such as words or images, may vary significantly in terms of both amplitude and latency. A unified approach is required to detect variable P300 signals, which facilitates BCI applications, as well as deepens the understanding of the P300 generation mechanism.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!