G. Birch

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Papers by G. Birch

Impact of an Energy Normalization Transform on the Performance of the LF-ASD Brain Computer Interface

Public information and communication technologies: Improving access or creating new barriers?

A wavelet-based approach for the extraction of event related potentials from EEG

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2000

Event Related Potentials (ERPs) are of interest to many researchers seeking knowledge about the f... more Event Related Potentials (ERPs) are of interest to many researchers seeking knowledge about the functions of the brain. ERPs are low-frequency events that are usually obscured in single trial analysis. To visualize these signals; most of the reliable solutions at the present time use the ensemble averages of many single trials. In this paper, a wavelet-based method called Statistical Coefficient Selection (SCS) is used for the extraction of ERPs from EEG signals. Unlike other waveletbased denoising methods, the current method does not focus on the wavelet coefficients of the signal itself. Instead, it selects the coefficients based on the statistical study of trials from training data set. Simulation results show the superiority of the proposed SCS method in extracting ERPs in comparison with other filtering approaches.

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Applying a Hybrid Genetic Algorithm in the Design of a Self-Paced Brain Interface with a Low False Positive Rate

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007

TOPICAL REVIEW: A survey of signal processing algorithms in brain computer interfaces based on electrical brain signals

Brain computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to ... more 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?

Further optimisation of a robust IMELDA speech recogniser for applications with severely degraded speech

Interspeech, 1992

Rethinking Disability in the Private Sector: Report from the Panel on Labour Market Opportunities for Persons with Disabilities

Automatic artefact detection in a self-paced brain-computer interface system

Proceedings of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 2011

An algorithm that detects various types of artefacts in a self-paced brain-computer interface is ... more An algorithm that detects various types of artefacts in a self-paced brain-computer interface is proposed. This method achieves similar performance to the state-of-art methods but has the following advantages, 1) being fully automatic (the threshold values are automatically found), 2) does not use additional EOG, EMG or frontal/temporal EEG channels, and 3) computationally inexpensive. The data were collected from the motor cortex areas using 15 EEG signals. The features extracted include the maximum amplitude of EEG signals and the stationary wavelet transform coefficients. To detect the artefacts, a simple threshold-based classifier is applied. The experimental results demonstrate that when detecting ocular and electrode movement artefacts, the method has a sensitivity (correctly detecting segments with artefacts) of 77.7%, and specificity (correctly detecting artefact-free segments) of 82.8%. For artefacts with more pronounced effects in the high frequency bands (e.g. facial muscle artefacts), the sensitivity and specificity are 83.5% and 70.3% respectively.

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Generalized Morphological Component Analysis for EEG source separation and artifact removal

2009 4th International IEEE/EMBS Conference on Neural Engineering, 2009

To remove artifacts from multi-channel Electroencephalography (EEG) data, we propose the use of G... more To remove artifacts from multi-channel Electroencephalography (EEG) data, we propose the use of Generalized Morphological Component Analysis (GMCA). GMCA separates the EEG signals into sources that have different morphological characteristics. Each source is sparse in an overcomplete dictionary, which is constructed using discrete cosine transform, Daubechies wavelet basis and Dirac basis. The sources related to artifacts are then removed. Semi-simulated EEG signals of movement-related potentials trials contaminated by eye-blink and muscle artifacts are used to evaluate the algorithm's performance. The performance of GMCA is compared with those of two other blind source separation algorithms, AMUSE and EFICA. The results demonstrate that GMCA successfully removes artifacts from EEG signals and the resulting distortions in both time and frequency domains are significantly lower than those of the other algorithms.

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Sparse spatial filter optimization for EEG channel reduction in brain-computer interface

2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008

Spatial filters are useful in discriminating different classes of electroencephalogram (EEG) sign... more Spatial filters are useful in discriminating different classes of electroencephalogram (EEG) signals such as those corresponding to motor activities. In the case of discriminating two classes of signals, EEG signals are projected onto a space where one class of signals is maximally scattered and the other is minimally scattered. This paper finds a minimal number of electrodes that can achieve the discrimination. Applying many electrodes is tedious and time-consuming. To reduce the number of electrodes, we propose inducing sparsity in the spatial filter. We reformulate the optimization problem in Common Spatial Patterns by introducing an 1-norm regularization term. Experimental results on five subjects show that the proposed method significantly reduces the number of electrodes while generating features with good discriminatory information. The number of electrodes on average, is reduced to 11% (of the 118 electrodes) while the average drop in the classification accuracy is only 3.8%.

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Facial EMG contamination of EEG signals: Characteristics and effects of spatial filtering

2008 3rd International Symposium on Communications, Control and Signal Processing, 2008

Facial electromyography (EMG) contamination of the electroencephalography (EEG) signals is a larg... more Facial electromyography (EMG) contamination of the electroencephalography (EEG) signals is a largely unresolved issue in brain-computer interface (BCI) research. Artifacts can obscure EEG features used in BCI control. Effective artifact detection and minimization require understanding of the artifacts. This study presents the spectral and the topographical properties of the artifacts caused by jaw clenching and eyebrow raising. Measures are introduced to quantify the effects of the artifacts on the EEG signals. We also compare the effectiveness of three spatial filtering methods (monopolar, small Laplacian and bipolar montage) in reducing the artifacts. Experiments on two subjects recorded the EEG signals during weak and moderate muscle contractions. The results show that the weak and the moderate contractions affect all frequencies at all locations (p < 0.01). This clearly demonstrates that EMG artifact detection and minimization are important not only for the BCIs focused on mu and beta rhythms, but also other BCIs that involve low frequency components. ANOVA analysis reveals that the small Laplacian and the bipolar montage are susceptible to these artifacts. Caution has to be exercised when choosing a spatial filtering method as some may be effective in extracting features but do not perform as well in the presence of artifacts.

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The Fast Pace of New Emerging Information and Communication Technologies: The Need for Regulations and Standards

Design and Use of Assistive Technology, 2010

... http://www.g3ict.com [5] Lew H, Leland D, Birch G (2009) Evaluation of mobile payment systems... more

Using a Multiple Classifier System for Improving the Performance of Asynchronous Brain Interface Systems

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006

A New Design of the Asynchronous Brain Computer Interface Using the Knowledge of the Path of Features

Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005., 2005

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain computer i... more The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain computer interface (BCI) technology for asynchronous control applications. The LF-ASD has the advantage that it is operational at any time and not only at specific defined periods. It is activated only when a user intends control, otherwise it maintains an inactive state output. The intended control results from

Improving the performance of the LF-ASD brain computer interface by means of genetic algorithm

Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004., 2004

... [9]T. M. Vaughan, J. R. Wolpaw, and E Donchin, EEG-Based Cotnmunication: Prospects and Probl... more

Fractal dimension-based EEG biofeedback system

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

Skip to Main Content. IEEE.org | IEEE Xplore Digital Library | IEEE Standards Association | Spect... more

Detection of Hand Extension Movements in the Context of a 3-State Asynchronous Brain Interface

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006

The low-frequency asynchronous switch design (LF-ASD) is a direct brain interface (BI) that detec... more The low-frequency asynchronous switch design (LF-ASD) is a direct brain interface (BI) that detects the presence of a specific finger movement in the ongoing EEG. Asynchronous interfaces have the advantage of being operational at all times and not only at specific system-defined periods. In this paper, we present the design of a 3-state asynchronous BI for the detection of two different movements from the ongoing EEG. The proposed 3-state asynchronous BI detects right and left hand extensions. Using data collected from two able-bodied individuals, it is shown that the error characteristics of the new system in detecting the presence of movement are significantly better than the 2-state LF-ASD, with true positive rate increases of up to 22.4 % for false positive rates in the 1-2 % range. An average performance of 61.5 % was achieved in differentiating between left and right hand movements.Ali Bashashati, Rabab K.

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A Hybrid Genetic Algorithm Approach for Improving the Performance of the LF-ASD Brain Computer Interface

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

An asynchronous Brain Computer Interface (BCI) continuously monitors the brain signals and is act... more An asynchronous Brain Computer Interface (BCI) continuously monitors the brain signals and is activated only when a user intends control. Initial results from an asynchronous system, the LF-ASD, designed by our group have shown promise, but the reported error rates are still high for most practical applications. To improve its performance, we propose user customization. Since energy normalization of all channels' signals is shown to significantly improve the performance of the system, we choose to customize the parameters related to this normalization. We apply a hybrid Genetic Algorithm (a Genetic Algorithm followed by a Local Search) to customize the size of the energy normalization windows. This is shown to significantly improve the results. For a fixed false positive rate of 2%, the improvement in the true positive rate was raised from 65.7% to 76.9 % in one subject and from 53.1% to 63.3% for another subject.

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Comparison of Using Mono-Polar and Bipolar Electroencephalogram (EEG) Electrodes for Detection of Right and Left Hand Movements in a Self-Paced Brain Computer Interface (BCI)

2007 Canadian Conference on Electrical and Computer Engineering, 2007

Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) ones have the advan... more Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) ones have the advantage of being operational at all times and not only at specific system-defined periods. A 3-state self-paced BCI is capable of detecting two different brain states (e.g. two movements) from the ongoing EEG. However, a 2-state one can only detect one brain state from the ongoing EEG. This study evaluates the performance of a 3-state self-paced BCI in detecting right and left hand movements. At first, we compare the performance of the system in differentiating between right and left hand movements using two different inputs: (1) mono-polar, and (2) bipolar electrode setting. Using bipolar electrode setting which yielded better performance than a mono-polar one, we evaluate the overall performance of the 3-state BCI system in a self-paced testing paradigm. Using data collected from two able-bodied individuals, it is shown that the average performance (true positive rate) of the system in detecting the presence of movements is 54.7% at a fixed false positive rate of 1%, and the average performance of the system in differentiating between right and left hand movements is 70.25%.

Recent Advances in the Design of a 3-State Self-Paced (Asynchronous) Brain Computer Interface

2007 3rd International IEEE/EMBS Conference on Neural Engineering, 2007

Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) BCIs have the advan... more Unlike synchronous brain computer interfaces (BCI), self-paced (asynchronous) BCIs have the advantage of being operational at all times. A 3-state self-paced BCI is capable of detecting two different brain states (e.g. two movements) from the ongoing EEG, while a 2-state one can only detect one brain state. This study improves the performance of a 3-state self-paced BCI designed to detect

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