Binarization Methods for Motor-Imagery Brain–Computer Interface Classification

Hersche, Michael; Benini, Luca; Rahimi, Abbas

doi:10.1109/jetcas.2020.3031698

Cited by 7 publications

(3 citation statements)

References 49 publications

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“…Computing with HD vectors can reduce the complexity of MANNs by computing with binary vectors ( 80 ). This recently proposed method reduced the number of parameters by replacing the fully connected layer of a convolutional neural network with a binary associative memory for EEG-based motor imagery brain–machine interfaces ( 81 ). Other interesting developments are the combination of concepts from HD and reservoir computing, which uses recurrent connections in a neural network to create a complex dynamical system ( 35 , 82 ).…”

Section: Discussionmentioning

confidence: 99%

A Primer on Hyperdimensional Computing for iEEG Seizure Detection

Schindler

Rahimi

2021

Front. Neurol.

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A central challenge in today's care of epilepsy patients is that the disease dynamics are severely under-sampled in the currently typical setting with appointment-based clinical and electroencephalographic examinations. Implantable devices to monitor electrical brain signals and to detect epileptic seizures may significantly improve this situation and may inform personalized treatment on an unprecedented scale. These implantable devices should be optimized for energy efficiency and compact design. Energy efficiency will ease their maintenance by reducing the time of recharging, or by increasing the lifetime of their batteries. Biological nervous systems use an extremely small amount of energy for information processing. In recent years, a number of methods, often collectively referred to as brain-inspired computing, have also been developed to improve computation in non-biological hardware. Here, we give an overview of one of these methods, which has in particular been inspired by the very size of brains' circuits and termed hyperdimensional computing. Using a tutorial style, we set out to explain the key concepts of hyperdimensional computing including very high-dimensional binary vectors, the operations used to combine and manipulate these vectors, and the crucial characteristics of the mathematical space they inhabit. We then demonstrate step-by-step how hyperdimensional computing can be used to detect epileptic seizures from intracranial electroencephalogram (EEG) recordings with high energy efficiency, high specificity, and high sensitivity. We conclude by describing potential future clinical applications of hyperdimensional computing for the analysis of EEG and non-EEG digital biomarkers.

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Section: Discussionmentioning

confidence: 99%

A Primer on Hyperdimensional Computing for iEEG Seizure Detection

Schindler

Rahimi

2021

Front. Neurol.

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“…Two challenges here are to increase the dimensionality and change the format of the neural network representations to conform with the HV format requirements. The former one is generally addressed by expanding the dimensionality, e.g., by random projection, possibly with a subsequent binarization by thresholding [138,296]. Some neural networks already produce binary vectors (see [285]), and the transformation into HVs was done by randomly repeating these binary vectors to get the necessary dimensionality.…”

Section: The Use Of Neural Network For Producing Hvsmentioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [322, 327] is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [223] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

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“…As a deep learning method, CNNs are often used to deal with binary classification problems with multi-dimension samples (Hersche et al, 2020). Essentially, the CNN algorithm mainly uses convolution, pooling and fully connected networks to alternately extraction, down-dimensioning, and fusion of features from multidimensional EEG data to achieve SA-level identification.…”

Section: Cnn Modified Modulementioning

confidence: 99%

EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm

et al. 2023

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Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots’ SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 < 0.1 in F and p = 0.042 < 0.05 in C), θ/(α + θ) (p = 0.048 < 0.05 in F and p = 0.026 < 0.05 in C) and (α + θ)/β (p = 0.046 < 0.05 in F and p = 0.012 < 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots’ cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

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Binarization Methods for Motor-Imagery Brain–Computer Interface Classification

Cited by 7 publications

References 49 publications

A Primer on Hyperdimensional Computing for iEEG Seizure Detection

A Primer on Hyperdimensional Computing for iEEG Seizure Detection

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

EEG-based analysis for pilots’ at-risk cognitive competency identification using RF-CNN algorithm

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