Using Combining Classifiers in Brain-Computer Interfacing

Brain-Computer Interface (BCI) provides an alternative communication channel with the outside world. For years، researchers have been trying to decode human neural signals and use them as controlling commands. The initial aim of BCI is to restore function to paralyzed patients suffering from various neurodegenrative diseases (e.g. amyotrophic lateral sclerosis، brainstem stroke and traumatic brain injury). Recently، BCI-controlled games have also gained popularity among community due to its novel design and streamlined appearance. Basically، there are two main groups of BCIs، namely، invasive and non-invasive. Although non-invasive methods are noise-prone and provide poor spatial resolution (as skull dampens signals resulting dispersed and blurred electromagnetic waves)، but due to its ease of use and convenience combined with not requiring any surgical operation، has attracted more attention from the research community. There are various approaches to brain-computer interfacing، among which the BCI based on motor imagery (MI) has been investigated in this thesis. Two frequency bandwaves، namely Mu (8-13Hz) and Beta (13-30 Hz)، comprise most of information encoding the state of imagined motor tasks. Bandpass filtering the signal and preprocessing the signal by performing various spatial and spectral filters، followed by feature extraction (e.g. Wavelet and Common Spatial Patterns) and feature selection (e.g. feature selection using Genetic Algorithms) techniques will try to yield supposedly reliable features for the classification block of BCI. In an effort to provide a comprehensive survey over various methods in brain-computer interfacing، numerous noteworthy techniques has been investigated in this dissertation. The thesis finishes by discussing the application of various combining-classifier methods to increase the performance obtained by individual classifiers.<br

Switching network for mixing experts with application to traffic sign recognition

The correct and robust recognition of traffic signs is indispensable to self-driving vehicles and driver-assistant systems. In this work, we propose and evaluate two network architectures for multi-expert decision systems that we test on a challenging Traffic Sign Recognition Benchmark dataset. The decision systems implement individual experts in the form of deep convolutional neural networks (CNNs). A gating network CNN acts as final decision unit and learns which individual expert CNNs are likely to contribute to an overall meaningful classification of a traffic sign. The gating network then selects the outputs of those individual expert CNNs to be fused to form the final decision. In this work we study the advantages and challenges of the proposed multi-expert architectures that in comparison to other network architectures allow for parallel training of individual experts with reduced datasets. Under the challenging conditions introduced by the benchmark dataset, the demonstrated multi-expert decision systems achieve a recognition performance that is superior to those of humans: with an accuracy of 99.10%, when training experts with the complete dataset and 98.94%, when individual experts are only trained with 36% of the training samples. Overall, our approach ranked fourth on the list of the applied approaches proposed for the German traffic sign Recognition Benchmark (GTSRB) dataset

Multiple classifier system for EEG signal classification with application to brain–computer interfaces

In this paper, we demonstrate the use of a<br>multiple classifier system for classification of electroencephalogram<br>(EEG) signals. The main purpose of this<br>paper is to apply several approaches to classify motor<br>imageries originating from the brain in a more robust<br>manner. For this study, dataset II from BCI competition III<br>was used. To extract features from the brain signal, discrete<br>wavelet transform decomposition was used. Then, several<br>classic classifiers were implemented to be utilized in the<br>multiple classifier system, which outperforms the reported<br>results of other proposed methods on the dataset. Also, a<br>variety of classifier combination methods along with<br>genetic algorithm feature selection were evaluated and<br>compared in order to diminish classification error. Our<br>results suggest that an ensemble system can be employed to<br>boost EEG classification accuracy