Brain-computer interface channel selection optimization using meta-heuristics and evolutionary algorithms

Producción CientíficaMany brain–computer interface (BCI) studies overlook the channel optimization due to its inherent complexity. However, a careful channel selection increases the performance and users’ comfort while reducing the cost of the system. Evolutionary meta-heuristics, which have demonstrated their usefulness in solving complex problems, have not been fully exploited yet in this context. The purpose of the study is two-fold: (1) to propose a novel algorithm to find an optimal channel set for each user and compare it with other existing meta-heuristics; and (2) to establish guidelines for adapting these optimization strategies to this framework. A total of 3 single-objective (GA, BDE, BPSO) and 4 multi-objective (NSGA-II, BMOPSO, SPEA2, PEAIL) existing algorithms have been adapted and tested with 3 public databases: ‘BCI competition III–dataset II’, ‘Center Speller’ and ‘RSVP Speller’. Dual-Front Sorting Algorithm (DFGA), a novel multi-objective discrete method especially designed to the BCI framework, is proposed as well. Results showed that all meta-heuristics outperformed the full set and the common 8-channel set for P300-based BCIs. DFGA showed a significant improvement of accuracy of 3.9% over the latter using also 8 channels; and obtained similar accuracies using a mean of 4.66 channels. A topographic analysis also reinforced the need to customize a channel set for each user. Thus, the proposed method computes an optimal set of solutions with different number of channels, allowing the user to select the most appropriate distribution for the next BCI sessions.Ministerio de Ciencia, Innovación y Universidades (project RTC2019-007350-1)Comisión Europea (project 0702_MIGRAINEE_2_E

A New Generation of Brain-Computer Interface Based on Riemannian Geometry

Based on the cumulated experience over the past 25 years in the field of Brain-Computer Interface (BCI) we can now envision a new generation of BCI. Such BCIs will not require training; instead they will be smartly initialized using remote massive databases and will adapt to the user fast and effectively in the first minute of use. They will be reliable, robust and will maintain good performances within and across sessions. A general classification framework based on recent advances in Riemannian geometry and possessing these characteristics is presented. It applies equally well to BCI based on event-related potentials (ERP), sensorimotor (mu) rhythms and steady-state evoked potential (SSEP). The framework is very simple, both algorithmically and computationally. Due to its simplicity, its ability to learn rapidly (with little training data) and its good across-subject and across-session generalization, this strategy a very good candidate for building a new generation of BCIs, thus we hereby propose it as a benchmark method for the field.Comment: 33 pages, 9 Figures, 17 equations/algorithm

A Tutorial on EEG Signal Processing Techniques for Mental State Recognition in Brain-Computer Interfaces

International audienceThis chapter presents an introductory overview and a tutorial of signal processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in Brain-Computer Interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial and temporal information from noisy EEG signals (e.g., Band Power features, spatial filters such as Common Spatial Patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., Linear Discriminant Analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyse EEG signals as well as to stress the key points to understand when performing such an analysis

Generalizing, Decoding, and Optimizing Support Vector Machine Classification

The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification. Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms

Electroencephalography (EEG)-based Brain-Computer Interfaces

International audienceBrain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). In this article, we aim at providing an accessible and up-to-date overview of EEG-based BCI, with a main focus on its engineering aspects. We notably introduce some basic neuroscience background, and explain how to design an EEG-based BCI, in particular reviewing which signal processing, machine learning, software and hardware tools to use. We present Brain Computer Interface applications, highlight some limitations of current systems and suggest some perspectives for the field

Electroencephalography brain computer interface using an asynchronous protocol

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in ful llment of the requirements for the degree of Master of Science. October 31, 2016.Brain Computer Interface (BCI) technology is a promising new channel for communication between humans and computers, and consequently other humans. This technology has the potential to form the basis for a paradigm shift in communication for people with disabilities or neuro-degenerative ailments. The objective of this work is to create an asynchronous BCI that is based on a commercial-grade electroencephalography (EEG) sensor. The BCI is intended to allow a user of possibly low income means to issue control signals to a computer by using modulated cortical activation patterns as a control signal. The user achieves this modulation by performing a mental task such as imagining waving the left arm until the computer performs the action intended by the user. In our work, we make use of the Emotiv EPOC headset to perform the EEG measurements. We validate our models by assessing their performance when the experimental data is collected using clinical-grade EEG technology. We make use of a publicly available data-set in the validation phase. We apply signal processing concepts to extract the power spectrum of each electrode from the EEG time-series data. In particular, we make use of the fast Fourier transform (FFT). Specific bands in the power spectra are used to construct a vector that represents an abstract state the brain is in at that particular moment. The selected bands are motivated by insights from neuroscience. The state vector is used in conjunction with a model that performs classification. The exact purpose of the model is to associate the input data with an abstract classification result which can then used to select the appropriate set of instructions to be executed by the computer. In our work, we make use of probabilistic graphical models to perform this association. The performance of two probabilistic graphical models is evaluated in this work. As a preliminary step, we perform classification on pre-segmented data and we assess the performance of the hidden conditional random fields (HCRF) model. The pre-segmented data has a trial structure such that each data le contains the power spectra measurements associated with only one mental task. The objective of the assessment is to determine how well the HCRF models the spatio-spectral and temporal relationships in the EEG data when mental tasks are performed in the aforementioned manner. In other words, the HCRF is to model the internal dynamics of the data corresponding to the mental task. The performance of the HCRF is assessed over three and four classes. We find that the HCRF can model the internal structure of the data corresponding to different mental tasks. As the final step, we perform classification on continuous data that is not segmented and assess the performance of the latent dynamic conditional random fields (LDCRF). The LDCRF is used to perform sequence segmentation and labeling at each time-step so as to allow the program to determine which action should be taken at that moment. The sequence segmentation and labeling is the primary capability that we require in order to facilitate an asynchronous BCI protocol. The continuous data has a trial structure such that each data le contains the power spectra measurements associated with three different mental tasks. The mental tasks are randomly selected at 15 second intervals. The objective of the assessment is to determine how well the LDCRF models the spatio-spectral and temporal relationships in the EEG data, both within each mental task and in the transitions between mental tasks. The performance of the LDCRF is assessed over three classes for both the publicly available data and the data we obtained using the Emotiv EPOC headset. We find that the LDCRF produces a true positive classification rate of 82.31% averaged over three subjects, on the validation data which is in the publicly available data. On the data collected using the Emotiv EPOC, we find that the LDCRF produces a true positive classification rate of 42.55% averaged over two subjects. In the two assessments involving the LDCRF, the random classification strategy would produce a true positive classification rate of 33.34%. It is thus clear that our classification strategy provides above random performance on the two groups of data-sets. We conclude that our results indicate that creating low-cost EEG based BCI technology holds potential for future development. However, as discussed in the final chapter, further work on both the software and low-cost hardware aspects is required in order to improve the performance of the technology as it relates to the low-cost context.LG201