Mutual Information-Based Electrode Selection Extended With Prior Knowledge For Use in Brain-Computer Interfacing

Moermans, Ruben; Wittevrongel, Benjamin; Hulle, Marc M. Van

doi:10.1109/bibm.2018.8621501

Cited by 4 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dependence is observed when the joint entropy is less than the sum of the marginal entropies. If the joint distribution of two random variables is given by p ( X , Y ) and their factored marginal distributions are p ( X ) and p ( Y ), the formula for mutual information, I ( X ; Y ) is as follows ( Moermans et al, 2018 ):…”

Section: Discussionmentioning

confidence: 99%

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Hernandez,

Kargarnovin,

Hejazi

et al. 2023

Front. Comput. Neurosci.

View full text Add to dashboard Cite

BackgroundConsidering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS.MethodsUsing the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients.ResultsA bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions.ConclusionAlthough nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.

show abstract

Section: Discussionmentioning

confidence: 99%

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Hernandez,

Kargarnovin,

Hejazi

et al. 2023

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…For multi-channel decoding, a greedy forward channel selection strategy was used which terminated when the average decoding accuracy from one to five stimulus repetitions of subsequent iterations no longer improved or when a decoding accuracy of 100% was reached. While other channel selection strategies have been suggested [ 76 ], greedy forward selection was chosen due to its intuitive design and straightforward implementation.…”

Section: Methodsmentioning

confidence: 99%

“…Candidate channel sets were scored using a fourfold cross-validation on the training epochs. While this heuristic approach is not guaranteed to find the optimal channel set, its simplicity has made it a widely adopted approach [ 76 ]. Of the 48 OPM channels recorded during the entire experiment, we manually selected 24 OPM sensors and 45 gradiometers located over the parieto-occipital scalp area prior to the experiment in order to speed up the channel selection procedure.…”

Section: Methodsmentioning

confidence: 99%

Practical real-time MEG-based neural interfacing with optically pumped magnetometers

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Brain-computer interfaces decode intentions directly from the human brain with the aim to restore lost functionality, control external devices or augment daily experiences. To combine optimal performance with wide applicability, high-quality brain signals should be captured non-invasively. Magnetoencephalography (MEG) is a potent candidate but currently requires costly and confining recording hardware. The recently developed optically pumped magnetometers (OPMs) promise to overcome this limitation, but are currently untested in the context of neural interfacing. Results In this work, we show that OPM-MEG allows robust single-trial analysis which we exploited in a real-time ‘mind-spelling’ application yielding an average accuracy of 97.7%. Conclusions This shows that OPM-MEG can be used to exploit neuro-magnetic brain responses in a practical and flexible manner, and opens up new avenues for a wide range of new neural interface applications in the future.

show abstract

“…From the preprocessed EEG recordings, the temporal dynamics of the SSVEP during each trial was estimated using the spatiotemporal beamforming principle. This extension of the spatial beamforming algorithm estimates the presence of a targeted spatiotemporal activation pattern (i.e., template), and was first introduced for the analysis of the N400 event-related potential in the context of semantic priming [16], but has since then been used in BCI studies on the P300 event-related potential [21], [22], code-modulated visual evoked potential [23], [24], [25], motion-onset visual evoked potential [26] and SSVEP [27], [17] paradigms.…”

Section: Spatiotemporal Beamformingmentioning

confidence: 99%

Monitoring temporal dynamics of steady-state visual evoked potentials

Wittevrongel

Hulle

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

Self Cite

View full text Add to dashboard Cite

The steady-state visual evoked potential (SSVEP) is an electrophysiological response to visual stimulation at a particular frequency. Despite its widespread adoption, primarily a spectral analysis of the recorded signal is performed, and variations in its temporal dynamics are often neglected or considered undesired. In this manuscript, we describe a method for monitoring the temporal dynamics of the SSVEP, and show its efficacy for detecting modifications of the stimulation profile on a single-trial basis. The anomalies elicited by single stimulus modifications were detected with an accuracy of 88%, demonstrating the reliability of the approach.

show abstract

Mutual Information-Based Electrode Selection Extended With Prior Knowledge For Use in Brain-Computer Interfacing

Cited by 4 publications

References 27 publications

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Practical real-time MEG-based neural interfacing with optically pumped magnetometers

Monitoring temporal dynamics of steady-state visual evoked potentials

Contact Info

Product

Resources

About