Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysis

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009.ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals such as EEG, EcOG, and MEG, and attempts to bridge the gap between thoughts and actions by providing control to physical devices that range from wheelchairs to computers. A crucial process for a BCI system is feature extraction, and many studies have been undertaken to find relevant information from a set of input signals. This thesis investigated feature extraction from EEG signals using two different approaches. Wavelet packet decomposition was used to extract information from the signals in their frequency domain, and cepstral analysis was used to search for relevant information in the cepstral domain. A BCI was implemented to evaluate the two approaches, and three classification techniques contributed to finding the effectiveness of each feature type. Data containing two-class motor imagery was used for testing, and the BCI was compared to some of the other systems currently available. Results indicate that both approaches investigated were effective in producing separable features, and, with further work, can be used for the classification of trials based on a paradigm exploiting motor imagery as a means of control.AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke inligting te vind. Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding, ’n metode wat gebruik word om die sein in die frekwensie gebied voor te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes te evalueer. Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese toestelle te beheer

Computer assisted interpretation of the human EEG: improving diagnostic efficiency and consistency in clinical reviews

Scalp electroencephalography (EEG) measures brain activity non-invasively by using electrodes on the scalp and capturing small electrical fluctuations caused by the firing of neurons. From these recordings, a clinical neurophysiologist can study the captured patterns and waveforms and determine if any abnormal brain activity exist. The disadvantage of EEG recordings are that they require an expert for interpretation and diagnosis. Not only this, but visual reviewing is also time consuming and a high degree of inter-rater variability exists between clinicians. Automated analysis by means of computerized algorithms can lessen the burden on visual reviews and provide more consistency in diagnostic reports. Given the complexity of the signal, automated analysis has only been partially implemented with limited clinical use. The main objective of this thesis was to find reliable computerized methods and efficient reviewing techniques that will assist with the review and interpretation of routine outpatient EEGs. To achieve this goal, algorithms were developed to automatically characterize five common EEG background properties: the posterior dominant peak frequency; reactivity; anterior-posterior gradients; symmetry; and the presence or absence of diffuse slow-wave activity. In addition to characterizing the background activity, an intuitive and efficient technique was also developed for the automated detection of inter-ictal epileptiform discharges. To evaluate our algorithms and reviewing techniques, a software application was developed and experienced neurologists and clinical neurophysiologists across the Netherlands were invited to evaluate and test the feasibility our approach. Very positive results were achieved. Additional testing and minor improvements are needed to bring this work io clinical practice, but the overall results show that the described methods and reviewing strategies, together with acceptance by clinicians, have been successfu

Quantification of the adult EEG background pattern

Objective Visual interpretation of EEG is time-consuming and not always consistent between reviewers. Our objective is to improve this by introducing guidelines and algorithms to quantify various properties, focussing on the background pattern in adult EEGs. Methods Five common properties were evaluated: (i) alpha rhythm frequency; (ii) reactivity; (iii) anterio–posterior gradients; (iv) asymmetries; and (v) diffuse slow-wave activity. A formal description was found for each together with a guideline and proposed quantitative algorithm. All five features were automatically extracted from routine EEG recordings. Modified time-frequency plots were calculated to summarize spectral and spatial characteristics. Visual analysis scores were obtained from diagnostic reports. Results Automated feature extraction was applied to 384 routine EEGs. Inter-rater agreement was calculated between visual and quantitative analysis using Fleiss’ kappa: κ = {(i) 0.60; (ii) 0.35; (iii) 0.19; (iv) 0.12; (v) 0.76}. The method is further illustrated with three representative examples of automated reports. Conclusions Automated feature extraction of several background EEG properties seems feasible. Inter-rater agreement differed between various features, ranging from slight to substantial. This may be related to the nature of various guidelines and inconsistencies in visual interpretation

Computer-assisted interpretation of the EEG background pattern: a clinical evaluation

Objective Interpretation of the EEG background pattern in routine recordings is an important part of clinical reviews. We evaluated the feasibility of an automated analysis system to assist reviewers with evaluation of the general properties in the EEG background pattern. Methods Quantitative EEG methods were used to describe the following five background properties: posterior dominant rhythm frequency and reactivity, anterior-posterior gradients, presence of diffuse slow-wave activity and asymmetry. Software running the quantitative methods were given to ten experienced electroencephalographers together with 45 routine EEG recordings and computer-generated reports. Participants were asked to review the EEGs by visual analysis first, and afterwards to compare their findings with the generated reports and correct mistakes made by the system. Corrected reports were returned for comparison. Results Using a gold-standard derived from the consensus of reviewers, inter-rater agreement was calculated for all reviewers and for automated interpretation. Automated interpretation together with most participants showed high (kappa > 0.6) agreement with the gold standard. In some cases, automated analysis showed higher agreement with the gold standard than participants. When asked in a questionnaire after the study, all participants considered computer-assisted interpretation to be useful for every day use in routine reviews. Conclusions Automated interpretation methods proved to be accurate and were considered to be useful by all participants. Significance Computer-assisted interpretation of the EEG background pattern can bring consistency to reviewing and improve efficiency and inter-rater agreement

Outline of the IED detection, grouping, and presentation steps.

<p>Multiple detections of inter-ictal activity are made using a database of matching template waveforms. Individual template detections are merged and grouped (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085180#pone-0085180-g003" target="_blank">Fig. 3</a>) to form IED nominations, and the nominations are presented for review in an iterative manner, ordered by nomination certainty.</p