A Bayesian Framework for Intent Detection and Stimulation Selection in SSVEP BCIs

Higger, Matt; Akçakaya, Murat; Nezamfar, Hooman; LaMountain, Gerald; Orhan, Umut; Erdoğmuş, Deni̇z

doi:10.1109/lsp.2014.2368952

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…Many EEG-based brain-computer interface paradigms present good average performance for communication [1,8,18,10,9] or HRI [47,27]. Yet they often require constant operator attention, add additional cognitive burden to the user, require many repeated prompts, and leverage user-specific classification algorithms [50,36,31,34,48].…”

Section: A Eeg-based Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

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Abstract-Control of robots in safety-critical tasks and situations where costly errors may occur is paramount for realizing the vision of pervasive human-robot collaborations. For these cases, the ability to use human cognition in the loop can be key for recuperating safe robot operation. This paper combines two streams of human biosignals, electrical muscle and brain activity via EMG and EEG, respectively, to achieve fast and accurate human intervention in a supervisory control task. In particular, this paper presents an end-to-end system for continuous rollingwindow classification of gestures that allows the human to actively correct the robot on demand, discrete classification of Error-Related Potential signals (unconsciously produced by the human supervisor's brain when observing a robot error), and a framework that integrates these two classification streams for fast and effective human intervention. The system also allows "plug-and-play" operation, demonstrating accurate performance even with new users whose biosignals have not been used for training the classifiers. The resulting hybrid control system for safety-critical situations is evaluated with 7 untrained human subjects in a supervisory control scenario where an autonomous robot performs a multi-target selection task.

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Section: A Eeg-based Methods For Human-robot Interactionmentioning

confidence: 99%

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

DelPreto

Salazar-Gomez

Gil

et al. 2018

Robotics: Science and Systems XIV

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“…ITR * is often lower than ITR because mistakes under a symmetric channel are uniformly distributed among all brain symbols, offering the weakest evidence (see noisy typewriter example in [37]). For further details on both definitions of ITR see [38], [39], [40], [41], [42]. …”

Section: Performance Metricsmentioning

confidence: 99%

Recursive Bayesian Coding for BCIs

Higger

Quivira

Akçakaya

et al. 2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain Computer Interfaces (BCI) seek to infer some task symbol, a task relevant instruction, from brain symbols, classifiable physiological states. For example, in a motor imagery robot control task a user would indicate their choice from a dictionary of task symbols (rotate arm left, grasp, etc.) by selecting from a smaller dictionary of brain symbols (imagined left or right hand movements). We examine how a BCI infers a task symbol using selections of brain symbols. We offer a recursive Bayesian decision framework which incorporates context prior distributions (e.g. language model priors in spelling applications), accounts for varying brain symbol accuracy and is robust to single brain symbol query errors. This framework is paired with Maximum Mutual Information (MMI) coding which maximizes a generalization of ITR. Both are applicable to any discrete task and brain phenomena (e.g. P300, SSVEP, MI). To demonstrate the efficacy of our approach we perform SSVEP “Shuffle” Speller experiments and compare our recursive coding scheme with traditional decision tree methods including Huffman coding. MMI coding leverages the asymmetry of the classifier’s mistakes across a particular user’s SSVEP responses; in doing so it offers a 33% increase in letter accuracy though it is 13% slower in our experiment.

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“…There are more articles about the classification algorithms innovation [122][123][124][125][126][127]. Reference [125] uses a semi-supervised training method with cooperation of two kinds of classifiers to build a integrated classifier.…”

Section: Feature Classificationmentioning

confidence: 99%

Review of Brain Computer Interface Components and Corresponding Research Topics

Wang¹,

Wang²,

Liang³

et al. 2016

Advances in Computer Science Research

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Abstract-This paper conducts a research on the literature of Brain Computer Interface(BCI) in recent years, inducing the four components of BCI and the research topics respectively, and providing a guide to newcomers for exploring BCI. BCI is divided into four parts including signal collection, feature extraction, feature classification and practical application. Papers of above four parts are retrieved respectively from the SCI database. Topics of all papers are concluded, based on which the research topics of BCI are assigned to the four parts of BCI. In topics of each part of BCI, similar topics are summarized to the unique topic. Finally some research topics may cover dozens of papers while some topics may cover only several papers. In each of the four BCI components, there are several topics attracting most of the attention. BCI still has a great research value and good prospects for study.

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A Bayesian Framework for Intent Detection and Stimulation Selection in SSVEP BCIs

Cited by 15 publications

References 23 publications

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Plug-and-Play Supervisory Control Using Muscle and Brain Signals for Real-Time Gesture and Error Detection

Recursive Bayesian Coding for BCIs

Review of Brain Computer Interface Components and Corresponding Research Topics

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