On-line EEG classification during externally-paced hand movements using a neural network-based classifier

Pfurtscheller, Gert; Kalcher, J.; Neuper, Christa; Flotzinger, Doris; Pregenzer, M.

doi:10.1016/s0013-4694(96)95689-8

Cited by 105 publications

(60 citation statements)

References 26 publications

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“…Gevins et al (1977) used thresholding of the EEG in different frequency bands to reduce ocular artefacts. A similar method was employed by Pfurtscheller et al (1996) and McFarland et al (1997) to reduce EMG artefact contamination in the EEG for BCI applications. An obvious disadvantage to these thresholding methods is that they do not allow the rejection of contaminated trials when EOG or EMG amplitude is small (Rowland, 1968;Croft and Barry, 2000).…”

Section: Thresholding Methodsmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Section: Thresholding Methodsmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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“…But the selection of these parameters is not very crucial. An advantage of the CSP method is that it does not require a priori selection of subject-specific frequency bands, as necessary for bandpower or frequency estimation methods (Pfurtscheller et al, 1996;McFarland et al, 1997b). …”

Section: Common Spatial Patterns (Csp)mentioning

confidence: 99%

Signal Processing and Classification Approaches for Brain-Computer Interface

Al-Ani¹,

Trad²

2010

Intelligent and Biosensors

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“…At this time, different brain signals can be used as input to a BCI: evoked potentials [4], slow cortical potentials (SCP) [5] or oscillatory EEG components [3,6]. Patients who suffer from severe motor impairments (e.g.…”

Section: Introductionmentioning

confidence: 99%

Rapid prototyping of an EEG-based brain-computer interface (BCI)

Guger

Schlögl

Neuper

et al. 2001

IEEE Trans. Neural Syst. Rehabil. Eng.

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303

171

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The Electroencephalogram (EEG) is modified by motor imagery and can be used by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) to communicate with their environment. Such a direct connection between the brain and the computer is known as an EEGbased brain-computer interface (BCI). This paper describes a new type of BCI system that uses Rapid Prototyping to enable a fast transition of various types of parameter estimation and classification algorithms to real-time implementation and testing. Rapid Prototyping is possible by using Matlab, Simulink and the Real-time Workshop. It is shown how to automate real-time experiments and perform the interplay between on-line experiments and off-line analysis. The system is able to process multiple EEG channels on-line and operates under Windows 95 in real-time on a standard PC without an additional digital signal processor (DSP) board.The BCI can be controlled over the Internet, LAN or modem.This BCI was tested on 3 subjects whose task it was to imagine either left or right hand movement. A classification accuracy between 70 and 95 % could be achieved with two EEG channels after some sessions with feedback using an adaptive autoregressive (AAR) model and linear discriminant analysis (LDA).2

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On-line EEG classification during externally-paced hand movements using a neural network-based classifier

Cited by 105 publications

References 26 publications

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Signal Processing and Classification Approaches for Brain-Computer Interface

Rapid prototyping of an EEG-based brain-computer interface (BCI)

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