Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

SSL for Auditory ERP-Based BCI

A brain–computer interface (BCI) is a communication tool that analyzes neural activity and relays the translated commands to carry out actions. In recent years, semi-supervised learning (SSL) has attracted attention for visual event-related potential (ERP)-based BCIs and motor-imagery BCIs as an effective technique that can adapt to the variations in patterns among subjects and trials. The applications of the SSL techniques are expected to improve the performance of auditory ERP-based BCIs as well. However, there is no conclusive evidence supporting the positive effect of SSL techniques on auditory ERP-based BCIs. If the positive effect could be verified, it will be helpful for the BCI community. In this study, we assessed the effects of SSL techniques on two public auditory BCI datasets—AMUSE and PASS2D—using the following machine learning algorithms: step-wise linear discriminant analysis, shrinkage linear discriminant analysis, spatial temporal discriminant analysis, and least-squares support vector machine. These backbone classifiers were firstly trained by labeled data and incrementally updated by unlabeled data in every trial of testing data based on SSL approach. Although a few data of the datasets were negatively affected, most data were apparently improved by SSL in all cases. The overall accuracy was logarithmically increased with every additional unlabeled data. This study supports the positive effect of SSL techniques and encourages future researchers to apply them to auditory ERP-based BCIs

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

Operationalization of Conceptual Imagery for BCIs

International audienceWe present a Brain Computer Interface (BCI) system in an asynchronous setting that allows classifying objects in their semantic categories (e.g. a hammer is a tool). For training, we use visual cues that are representative of the concepts (e.g. a hammer image for the concept of hammer). We evaluate the system in an offline synchronous setting and in an online asynchronous setting. We consider two scenarios: the first one, where concepts are in close semantic families (10 subjects) and the second where concepts are from distinctly different categories (10 subjects). We find that both have classification accuracies of 70% and above, although more distant conceptual categories lead to 5% more in classification accuracy

Brain–computer interfacing with interactive systems-Case study 2

info:eu-repo/semantics/publishedVersio

Improvement of EEG based brain computer interface by application of tripolar electrodes and independent component analysis

For persons with severe disabilities, a brain computer interface (BCI) may be a viable means of communication, with scalp-recorded electroencephalogram (EEG) being the most common signal employed in the operation of a BCI. Various electrode configurations can be used for EEG recording, one of which was a set of concentric rings that was referred to as a Laplacian electrode. It has been shown that Lapalacian EEG could improve classification in EEG recognition, but the complete advantages of this configuration have not been established. This project included two parts. First, a modeling study was performed using Independent Component Analysis (ICA) to prove that tripolar electrodes could provide better EEG signal for BCI. Next, human experiments were performed to study the application of tripolar electrodes in a BCI model to show that the application of tripolar electrodes and data-segment related parameter selection can improve EEG classification ratio for BCI. In the first part of work, an improved four-layer anisotropic concentric spherical head computer model was programmed, then four configurations of time-varying dipole signals were used to generate the scalp surface signals that would be obtained with tripolar and disc electrodes. Four important EEG artifacts were tested: eye blinking, cheek movements, jaw movements and talking. Finally, a fast fixed-point algorithm was used for signal-independent component analysis (ICA). The results showed that signals from tripolar electrodes generated better ICA separation than signals from disc electrodes for EEG signals, suggesting that tripolar electrodes could provide better EEG signal for BCI. The human experiments were divided into three parts: improvement of the data acquirement system by application of tripolar concentric electrodes and related circuit; development of pre-feature selection algorithm to improve BCI EEG signal classification; and an autoregressive (AR) model and Mahalanobis distance-based linear classifier for BCI classification. In the work, tripolar electrodes and corresponding data acquisition system were developed. Two sets of left/right hand motor imagery EEG signals were acquired. Then the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. The pre-feature selection methods were developed and applied to four data segment-related parameters: the length of the data segment in each trial (LDS), its starting position (SPD), the number of trials (NT) and the AR model order (AR Order). The study showed that, compared to the classification ratio (CR) without parameter selection, the CR was significantly different with an increase by 20% to 30% with proper selection of these data-segment-related parameter values and that the optimum parameter values were subject-dependent, which suggests that the data-segment-related parameters should be individualized when building models for BCI. The experiments also showed that that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes

Signal Processing Combined with Machine Learning for Biomedical Applications

The Master’s thesis is comprised of four projects in the realm of machine learning and signal processing. The abstract of the thesis is divided into four parts and presented as follows, Abstract 1: A Kullback-Leibler Divergence-Based Predictor for Inter-Subject Associative BCI. Inherent inter-subject variability in sensorimotor brain dynamics hinders the transferability of brain-computer interface (BCI) model parameters across subjects. An individual training session is essential for effective BCI control to compensate for variability. We report a Kullback-Leibler Divergence (KLD)-based predictor for inter-subject associative BCI. An online dataset comprising left/right hand, both feet, and tongue motor imagery tasks was used to show correlation between the proposed inter-subject predictor and BCI performance. Linear regression between the KLD predictor and BCI performance showed a strong inverse correlation (r = -0.62). The KLD predictor can act as an indicator for generalized inter-subject associative BCI designs. Abstract 2: Multiclass Sensorimotor BCI Based on Simultaneous EEG and fNIRS. Hybrid BCI (hBCI) utilizes multiple data modalities to acquire brain signals during motor execution (ME) tasks. Studies have shown significant enhancements in the classification of binary class ME-hBCIs; however, four-class ME-hBCI classification is yet to be done using multiclass algorithms. We present a quad-class classification of ME-hBCI tasks from simultaneous EEG-fNIRS recordings. Appropriate features were extracted from EEG-fNIRS signals and combined for hybrid features and classified with support vector machine. Results showed a significant increase in hybrid accuracy over single modalities and show hybrid method’s performance enhancement capability. Abstract 3: Deep Learning for Improved Inter-Subject EEG-fNIRS Hybrid BCI Performance. Multimodality based hybrid BCI has become famous for performance improvement; however, the inherent inter-subject and inter-session variation between participants brain dynamics poses obstacles in achieving high performance. This work presents an inter-subject hBCI to classify right/left-hand MI tasks from simultaneous EEG-fNIRS recordings of 29 healthy subjects. State-of-art features were extracted from EEG-fNIRS signals and combined for hybrid features, and finally, classified using deep Long short-term memory classifier. Results showed an increase in the inter-subject performance for the hybrid system while making the system more robust to brain dynamics change and hints to the feasibility of EEG-fNIRS based inter-subject hBCI. Abstract 4: Microwave Based Glucose Concentration Classification by Machine Learning. Non-invasive blood sugar measurement attracts increased attention in recent years, given the increase in diabetes-related complications and inconvenience in the traditional ways using blood. This work utilized machine learning (ML) algorithms to classify glucose concentration (GC) from the measured broadband microwave scattering signals (S11). An N-type microwave adapter pair was utilized to measure the sweeping frequency scattering-parameter (S-parameter) of the glucose solutions with GC varying from 50-10,000 dg/dL. Dielectric parameters were retrieved from the measured wideband complex S-parameters based on the modified Debye dielectric dispersion model. Results indicate that the best algorithm can achieve a perfect classification accuracy and suggests an alternate way to develop a GC detection method using ML algorithms