Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces

By Ola Friman, Ivan Volosyak, Axel Gräser. IEEE Transactions on Biomedical Engineering, 2007

570 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/5e0f1f19ff8b4801d83a13e87d72e74c14d7e621

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

A Survey of Stimulation Methods Used in SSVEP-Based BCIs (2010, unknown): “Additionally, spatial ﬁlters that combine several lead signals into one channel [34] can be used to increase the SSVEP energy enough so it can e ﬀ ectively be used in a BCI.”
Steady-state visually evoked potentials: focus on essential paradigms and future perspectives. (2010, background): “However, doubts have been raised about ICA, especially in the context of BCI systems; statistical independence may not be a valid assumption, and it is not always obvious how to rank the independent sources (Friman et al., 2007).”
Brain-Computer Interfaces Based on Visual Evoked Potentials (2008, methodology): “Under this circumstance, it is acceptable to use more electrodes to acquire more sufficient data to fulfill the detection of SSVEP signals with multichannel data analysis approaches, e.g., spatial filtering techniques described in [ 28 ] and the canonical correlation analysis method presented in [29”
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? (2010, methodology): “the best arrangement of characters in the display, were determined through prior work [24]–[26] and pilot work [27].”
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (2009, methodology): “Friman et al [9] proposed a minimum energy method (MEC) which shows many advantages such as high detection accuracy and no calibration data.”
Frequency Recognition in SSVEP-Based BCI using Multiset Canonical Correlation Analysis (2013, methodology): “Although original SSVEP responses present relatively stable spectrums over time, they are likely to be contaminated by ongoing EEG activities and other background noises 20 , 21 .”
Transfer Learning for EEG-Based Brain–Computer Interfaces: A Review of Progress Made Since 2016 (2020, unknown): “The following observations and conclusions can be made, which may point to some future research directions: 1) Among the three classic BCI paradigms (MI, ERP and SSVEP), SSVEP seems to receive the least amount of attention.”
How many people are able to control a P300-based brain-computer interface (BCI)? (2009, unknown): “For example, slow cortical potentials [1], oscillations in alpha and beta range [6,11], steady-state visual evoked potentials (SSVEP) [2,17] and the P300 event-related potential [13] have all been used successfully for BCI control.”
Brain-Computer Interfaces: A Gentle Introduction (2009, background): “There are a few papers that report BCIs with a high ITR, ranging from 30 bits/min [26, 27] to slightly above 60 bits/min [28] and, most recently, over 90 bits per minute [29].”
A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control (2013, methodology): “For each segment, we use the minimum energy combination (MEC) to combine the signals from multiple channels [34].”
A Benchmark Dataset for SSVEP-Based Brain–Computer Interfaces (2017, methodology): “For detecting SSVEPs, researchers have developed many algorithms [14]–[17], which were evaluated with data collected by themselves.”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, background): “For example, spatial ﬁ ltering approaches such as the canonical correlation analysis (CCA) (Lin et al 2007) and minimum energy combination (MEC) (Friman et al 2007) methods have been developed to enhance the SNR of SSVEPs.”
A Comparison Study of Canonical Correlation Analysis Based Methods for Detecting Steady-State Visual Evoked Potentials (2015, methodology): “The widely used spatial filtering methods in SSVEP-based BCIs include canonical correlation analysis (CCA) [15], minimum energy combination (MEC) [16], and common spatial pattern (CSP) [17].”