Brain–Computer Interface

Nam, Chang S.; Choi, Inchul; Wadeson, Amy; Whang, Mincheol

doi:10.1201/9781351231954-1

Cited by 12 publications

(1 citation statement)

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“…In this paper, we focus on EEG which excels in terms of temporal resolution, price, and portability, as well as low risk from a health perspective. However, compared to many of the other technologies, EEG lacks substantially in terms of spatial resolution (Nam et al, 2018 ).…”

Section: Materials and Equipmentmentioning

confidence: 99%

An open-source human-in-the-loop BCI research framework: method and design

Gemborn Nilsson,

Tufvesson,

Heskebeck

et al. 2023

Front. Hum. Neurosci.

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Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil).

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Section: Materials and Equipmentmentioning

confidence: 99%

An open-source human-in-the-loop BCI research framework: method and design

Gemborn Nilsson,

Tufvesson,

Heskebeck

et al. 2023

Front. Hum. Neurosci.

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Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI

Chevallier

Kalunga²,

Barthélemy³

2020

Neuroinform

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Background The first generation of brain-computer interfaces (BCI) classifies multi-channel electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second generation directly classifies covariance matrices estimated on EEG signals, based on straightforward algorithms such as the minimum-distance-to-Riemannian-mean (MDRM). Classification results vary greatly depending on the chosen Riemannian distance or divergence, whose definitions and reference implementations are spread across a wide mathematical literature.Methods This paper reviews all the Riemannian distances and divergences to process covariance matrices, with an implementation compatible with BCI constraints. The impact of using different metrics is assessed on a steady-state visually evoked potentials (SSVEP) dataset, evaluating centers of classes and classification accuracy.Results and Conclusions Riemannian approaches embed crucial properties to process EEG data. The Riemannian centers of classes outperform Euclidean ones both in offline and online setups. Some Riemannian

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Automatic control of reactive brain computer interfaces

Tufvesson,

Heskebeck

2024

IFAC Journal of Systems and Control

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Brain–Computer Interface

Cited by 12 publications

References 1 publication

An open-source human-in-the-loop BCI research framework: method and design

An open-source human-in-the-loop BCI research framework: method and design

Review of Riemannian Distances and Divergences, Applied to SSVEP-based BCI

Automatic control of reactive brain computer interfaces

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