Brain-computer interfaces for communication and control.

In this paper we review classification algorithms used to design brain–computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.

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A review of classification algorithms for EEG-based brain–computer interfaces

The brain's electrical signals enable people without muscle control to physically interact with the world.

Brain-computer interfaces for communication and control

Objective: Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. The time is therefore ripe for an updated review of EEG classification algorithms for BCIs. Approach: We surveyed the BCI and machine learning literature from 2007 to 2017 to identify the new classification approaches that have been investigated to design BCIs. We synthesize these studies in order to present such algorithms, to report how they were used for BCIs, what were the outcomes, and to identify their pros and cons. Main results: We found that the recently designed classification algorithms for EEG-based BCIs can be divided into four main categories: adaptive classifiers, matrix and tensor classifiers, transfer learning and deep learning, plus a few other miscellaneous classifiers. Among these, adaptive classifiers were demonstrated to be generally superior to static ones, even with unsupervised adaptation. Transfer learning can also prove useful although the benefits of transfer learning remain unpredictable. Riemannian geometry-based methods have reached state-of-the-art performances on multiple BCI problems and deserve to be explored more thoroughly, along with tensor-based methods. Shrinkage linear discriminant analysis and random forests also appear particularly useful for small training samples settings. On the other hand, deep learning methods have not yet shown convincing improvement over state-of-the-art BCI methods. Significance: This paper provides a comprehensive overview of the modern classification algorithms used in EEG-based BCIs, presents the principles of these Review of Classification Algorithms for EEG-based BCI 2 methods and guidelines on when and how to use them. It also identifies a number of challenges to further advance EEG classification in BCI.

https://iopscience.iop.org/article/10.1088/1741-2552/aab2f2/pdf

A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces: A 10-year Update

Brain–computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention? S This article has associated online supplementary data files

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A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals.

This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals.

Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

EEG signals are modeled using single-channel and multi-channel autoregressive (AR) techniques. The coefficients of these models are used to classify EEG data into one of two classes corresponding to the mental task the subjects are performing. A neural network is trained to perform the classification. When applying a trained network to test data, the authors find that the multivariate AR representation performs slightly better, resulting in an average classification accuracy of about 91%.

Discriminating mental tasks using EEG represented by AR models

The fifth generation (5G) of cellular networks is significantly more complex than its predecessors due to several factors, such as increased cell density, differentiated service requirements, and coexistence with legacy networks. As a result, traditional operation and management (O&M) solutions, which heavily rely on human intervention, are no longer feasible to support such complex networks at reasonable operating expense (OPEX). Over the past few years, the telecommunication industry has come to the realization that leveraging artificial intelligence (AI) technology to enable a fully automated network O&M is a must to lowering OPEX and enhancing network key performance indicators (KPIs) for 5G, Beyond 5G (B5G), and the sixth generation (6G) of cellular networks. There have been numerous research efforts from both industry and academia to develop AI-powered network automation solutions. Many telecommunication operators and vendors have already adopted AI technology to automate some repetitive operational tasks and reduce reliance on personnel experience, such as cell planning, network deployment simplification, fault detection, and KPI optimization. While there has been notable progress for certain network O&M applications, the development of network automation solutions still faces several unique technical challenges that arise from telecommunication fields, including overwhelming network complexity, massive and diverse proprietary data, lack of industry-wide standards for radio access network (RAN) interfaces, and scarcity of labeled datasets. 

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S. Shamsunder

Papers

Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

Discriminating mental tasks using EEG represented by AR models

Guest Editorial: AI-Powered Telco Network Automation: 5G Evolution and 6G