A novel multiclass-based framework for P300 detection in BCI matrix speller: Temporal EEG patterns of non-target trials vary based on their position to previous target stimuli-Reference-Cited by-同舟云学术

A novel multiclass-based framework for P300 detection in BCI matrix speller: Temporal EEG patterns of non-target trials vary based on their position to previous target stimuli

Published:2023-08 Issue: Volume:123 Page:106381
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Norizadeh Cherloo Mohammad,Mijani Amir Mohammad,Zhan Liang^ORCID,Daliri Mohammad Reza

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference56 articles.

1. A comparison of feature extraction strategies using wavelet dictionaries and feature selection methods for single trial P300-based BCI;Acevedo;Med. Biol. Eng. Comput.,2019

2. Gaze-independent BCI-spelling using rapid serial visual presentation (RSVP);Acqualagna;Clin. Neurophysiol.,2013

3. A few filters are enough: Convolutional neural network for P300 detection;Alvarado-Gonzalez;Neurocomputing,2021

4. Pairwise and variance based signal compression algorithm (PVBSC) in the P300 based speller systems using EEG signals;Arican;Comput. Methods Programs Biomed.,2019

5. Enhancing P300 detection using a band-selective filter bank for a visual P300 speller;Blanco-Díaz;IRBM,2023

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Convolutional Dynamically Convergent Differential Neural Network for Brain Signal Classification;IEEE Transactions on Neural Networks and Learning Systems;2024

2. Temporally Local Weighting-Based Phase-Locked Time-Shift Data Augmentation Method for Fast-Calibration SSVEP-BCI;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

3. Convolutional Neural Network-Based Classification of Steady-State Visually Evoked Potentials with Limited Training Data;Applied Sciences;2023-12-18