Brain–Computer Interfaces

Millán, José del R.

doi:10.1002/9781118628522.ch12

Cited by 14 publications

(3 citation statements)

References 77 publications

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“…Motor learning-based controls use closed-loop feedback to train human motor systems to interact efficiently with a mapping function transforming sEMG-related inputs to control outputs. This method is common in brain-machine interfaces [256][257][258], which utilize brain plasticity to encourage users to associate thoughts with controls [259][260][261]. With sEMG, it is expectated that humans learn to associate commands with limb motion, and many studies invoke simple control schemes to analyze the learning process as users perform a given set of tasks.…”

Section: Motor Learning-based Controlmentioning

confidence: 99%

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Ison

Artemiadis²

2014

J. Neural Eng.

164

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Myoelectric control is filled with potential to significantly change human-robot interaction due to the ability to non-invasively measure human motion intent. However, current control schemes have struggled to achieve the robust performance that is necessary for use in commercial applications. As demands in myoelectric control trend toward simultaneous multifunctional control, multi-muscle coordinations, or synergies, play larger roles in the success of the control scheme. Detecting and refining patterns in muscle activations robust to the high variance and transient changes associated with surface electromyography is essential for efficient, user-friendly control. This article reviews the role of muscle synergies in myoelectric control schemes by dissecting each component of the scheme with respect to associated challenges for achieving robust simultaneous control of myoelectric interfaces. Electromyography recording details, signal feature extraction, pattern recognition and motor learning based control schemes are considered, and future directions are proposed as steps toward fulfilling the potential of myoelectric control in clinically and commercially viable applications.

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Section: Motor Learning-based Controlmentioning

confidence: 99%

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Ison

Artemiadis²

2014

J. Neural Eng.

164

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“…Brain-computer interfaces (BCIs) are cutting-edge systems aimed at identifying subjects' intention from measurements of brain activity [ 1 – 3 ]. A major clinical challenge in BCI research has been to develop systems capable of restoring communication in those people who, because of brainstem strokes, cerebral palsies, brain/spinal cord injuries, or progressive neurodegenerative diseases (such as amyotrophic lateral sclerosis, ALS), have lost the control of nearly all voluntary muscles but still retain cognition and sensation [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface

D’Avanzo

Tonin

Piccione

et al. 2014

Computational and Mathematical Methods in Medicine

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A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of system's setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the system's accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

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“…Currently an extension of the work would be to implement the HTP algorithm [25] as a learning rule which updates a set of weights which could be used for input-output associations for the feed forward spiking neural network. The results have implications that could be used for several direct and non-invasive BCI requiring physiological signals to be used for operating devices [38].…”

Section: Discussionmentioning

confidence: 96%

Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

Medini

Zacharia

Nair

et al. 2015

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Spiking neural encoding models allow classification of real world tasks to suit for brain-machine interfaces in addition to serving as internal models. We developed a new spike encoding model inspired from cerebellum granular layer and tested different classification techniques like SVM, Naïve Bayes, MLP for training spiking neural networks to perform pattern recognition tasks on encoded datasets. As a precursor to spiking networkbased pattern recognition, in this study, real world datasets were encoded into spike trains. The objective of this study was to encode information from datasets into spiking neuron patterns that were relevant for spiking neural networks and for conventional machine learning algorithms. In this initial study, we present a new approach similar to cerebellum granular layer encoding and compared it with BSA encoding techniques. We have also compared the efficiency of the encoded dataset with different datasets and with standard machine learning algorithms.

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Brain–Computer Interfaces

Cited by 14 publications

References 77 publications

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control

Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface

Spike encoding for pattern recognition: Comparing cerebellum granular layer encoding and BSA algorithms

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