Improving the Generalisability of Brain Computer Interface Applications via Machine Learning and Search-Based Heuristics

Brain Computer Interfaces (BCI) are a domain of hardware/software in which a user can interact with a machine without the need for motor activity, communicating instead via signals generated by the nervous system. These interfaces provide life-altering benefits to users, and refinement will both allow their application to a much wider variety of disabilities, and increase their practicality. The primary method of acquiring these signals is Electroencephalography (EEG). This technique is susceptible to a variety of different sources of noise, which compounds the inherent problems in BCI training data: large dimensionality, low numbers of samples, and non-stationarity between users and recording sessions. Feature Selection and Transfer Learning have been used to overcome these problems, but they fail to account for several characteristics of BCI. This thesis extends both of these approaches by the use of Search-based algorithms. Feature Selection techniques, known as Wrappers use ‘black box’ evaluation of feature subsets, leading to higher classification accuracies than ranking methods known as Filters. However, Wrappers are more computationally expensive, and are prone to over-fitting to training data. In this thesis, we applied Iterated Local Search (ILS) to the BCI field for the first time in literature, and demonstrated competitive results with state-of-the-art methods such as Least Absolute Shrinkage and Selection Operator and Genetic Algorithms. We then developed ILS variants with guided perturbation operators. Linkage was used to develop a multivariate metric, Intrasolution Linkage. This takes into account pair-wise dependencies of features with the label, in the context of the solution. Intrasolution Linkage was then integrated into two ILS variants. The Intrasolution Linkage Score was discovered to have a stronger correlation with the solutions predictive accuracy on unseen data than Cross Validation Error (CVE) on the training set, the typical approach to feature subset evaluation. Mutual Information was used to create Minimum Redundancy Maximum Relevance Iterated Local Search (MRMR-ILS). In this algorithm, the perturbation operator was guided using an existing Mutual Information measure, and compared with current Filter and Wrapper methods. It was found to achieve generally lower CVE rates and higher predictive accuracy on unseen data than existing algorithms. It was also noted that solutions found by the MRMR-ILS provided CVE rates that had a stronger correlation with the accuracy on unseen data than solutions found by other algorithms. We suggest that this may be due to the guided perturbation leading to solutions that are richer in Mutual Information. Feature Selection reduces computational demands and can increase the accuracy of our desired models, as evidenced in this thesis. However, limited quantities of training samples restricts these models, and greatly reduces their generalisability. For this reason, utilisation of data from a wide range of users is an ideal solution. Due to the differences in neural structures between users, creating adequate models is difficult. We adopted an existing state-of-the-art ensemble technique Ensemble Learning Generic Information (ELGI), and developed an initial optimisation phase. This involved using search to transplant instances between user subsets to increase the generalisability of each subset, before combination in the ELGI. We termed this Evolved Ensemble Learning Generic Information (eELGI). The eELGI achieved higher accuracy than user-specific BCI models, across all eight users. Optimisation of the training dataset allowed smaller training sets to be used, offered protection against neural drift, and created models that performed similarly across participants, regardless of neural impairment. Through the introduction and hybridisation of search based algorithms to several problems in BCI we have been able to show improvements in modelling accuracy and efficiency. Ultimately, this represents a step towards more practical BCI systems that will provide life altering benefits to users

Channel Selection Procedure using Riemannian distance for BCI applications

International audienceThis article describes a new algorithm to select a subset of electrodes in BCI experiments. It is illustrated on a two-class motor imagery paradigm. The proposed approach is based on the Riemannian distance between spatial covariance matrices which allows to indirectly assess the discriminability between classes. Sensor selection is automatically done using a backward elimination principle. The method is tested on the dataset IVa from BCI competition III. The identified subsets are both consistent with neurophysiological principles and effective, achieving optimal performances with a reduced number of channels

A chromatic transient visual evoked potential based encoding/decoding approach for brain-computer interface

This paper presents a new encoding/decoding approach to brain-computer interface (BCI) based on chromatic transient visual evoked potential (CTVEP). The proposed CTVEP-based encoding/decoding approach is designed to provide a safer and more comfortable stimulation method than the conventional VEP-based stimulation methods for BCI without loss of efficiency. For this purpose, low-frequency isoluminant chromatic stimuli are time-encoded to serve as different input commands for BCI control, and the superior comfortableness of the proposed stimulation method is validated by a survey. A combination of diversified signal processing techniques are further employed to decode the information from CTVEP. Based on experimental results, a properly designed configuration of the CTVEP-based stimulation method and a tailored signal processing framework are developed. It is demonstrated that high performance (at information transfer rate: 58.0 bits/min, accuracy: 94.9%, false alarm rate: 1.3%) for BCI can be achieved by means of the CTVEP-based encoding/decoding approach. It turns out that to achieve such good performance, only simple signal processing algorithms with very low computational complexity are required, which makes the method suitable for the development of a practical BCI system. A preliminary prototype of such a system has been implemented with demonstrated applicability. © 2011 IEEE.published_or_final_versio

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose

Signals from Intraventricular Depth Electrodes Can Control a Brain-Computer Interface

A Brain-Computer Interface (BCI) is a device that enables severely disabled people to communicate and interact with their environments using their brain waves. Most research investigating BCI in humans have used scalp-recorded electroencephalography (EEG). We have recently demonstrated that signals from intracranial electrocorticography (ECoG) and stereotactic depth electrodes (SDE) in the hippocampus can be used to control a BCI P300 Speller paradigm. We report a case in which stereotactic depth electrodes positioned in the ventricle were able to obtain viable signals for a BCI. Our results demonstrate that event-related potentials from intraventricular electrodes can be used to reliably control the P300 Speller BCI paradigm

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

Effective EEG analysis for advanced AI-driven motor imagery BCI systems

Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets.Developing effective signal processing for brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) involves factoring in three aspects of functionality: classification performance, execution time, and the number of data channels used. The contributions in this thesis are centered on these three issues. Contributions are focused on the classification of motor imagery (MI) data, which is generated during imagined movements. Typically, EEG time-series data is segmented for data augmentation or to mimic buffering that happens in an online BCI. A multi-segment decision fusion approach is presented, which takes consecutive temporal segments of EEG data, and uses decision fusion to boost classification performance. It was computationally lightweight and improved the performance of four conventional classifiers. Also, an analysis of the contributions of electrodes from different scalp regions is presented, and a subset of channels is recommended. Sparse learning (SL) classifiers have exhibited strong classification performance in the literature. However, they are computationally expensive. To reduce the test-set execution times, a novel EEG classification pipeline consisting of a genetic-algorithm (GA) for channel selection and a dictionary-based SL module for classification, called GABSLEEG, is presented. Subject-specific channel selection was carried out, in which the channels are selected based on training data from the subject. Using the GA-recommended subset of EEG channels reduced the execution time by 60% whilst preserving classification performance. Although subject-specific channel selection is widely used in the literature, effective subject-independent channel selection, in which channels are detected using data from other subjects, is an ideal aim because it leads to lower training latency and reduces the number of electrodes needed. A novel convolutional neural network (CNN)-based subject-independent channels selection method is presented, called the integrated channel selection (ICS) layer. It performed on-a-par with or better than subject-specific channel selection. It was computationally efficient, operating 12-17 times faster than the GA channel selection module. The ICS layer method was versatile, performing well with two different CNN architectures and datasets