Improvements of Adaptive Filtering by Optimal Projection to filter different artifact types on long duration EEG recordings

• Published in: Computer methods and programs in biomedicine Vol. 108; no. 1; pp. 234 - 249
• Main Authors: Boudet, S, Peyrodie, L, Forzy, G, Pinti, A, Toumi, H, Gallois, P
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 01-10-2012; Elsevier
• Subjects: Adaptation, Physiological; Adaptive filters; Amplitudes; Applied sciences; Automatic filtering; Biological and medical sciences; Brain; Computer Science; CSP; Detection, estimation, filtering, equalization, prediction; EEG; Electrode artifacts; Electrodiagnosis. Electric activity recording; Electroencephalography; Engineering Sciences; Epilepsy; Exact sciences and technology; Filtering; Filtration; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Information, signal and communications theory; Internal Medicine; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Muscle artifacts; Nervous system; Nervous system (semeiology, syndromes); Neurology; Ocular artifacts; Other; Projection; Recording; Signal and communications theory; Signal and Image processing; Signal, noise; Telecommunications and information theory; CSP; EEG; Epilepsy; Ocular artifacts; Automatic filtering; Electrode artifacts; Muscle artifacts; Brain; Source separation; Time frequency domain method; Central nervous system; Adaptive filtering; Quantization; Constraint satisfaction; Artefact; Electroencephalography; Encephalon; Electrodes; Projection method; Orthogonalization; Medical application; Human; Optimal filter; Independent component analysis; Duration; Blind separation; Time window; Optimal filtering; AFOP-DAFOP; artifact rejection; ICA
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2012.04.005

Abstract Adaptive Filtering by Optimal Projection (AFOP) is an automatic method for reducing ocular and muscular artifacts on electro-encephalographic (EEG) recordings. This paper presents two additions to this method: an improvement of the stability of ocular artifact filtering and an adaptation of the method for filtering electrode artifacts. With these improvements, it is possible to reduce almost all the current types of artifacts, while preserving brain signals, particularly those characterising epilepsy. This generalised method consists of dividing the signal into several time–frequency windows, and in applying different spatial filters to each. Two steps are required to define one of these spatial filters: the first step consists of defining artifact spatial projection using the Common Spatial Pattern (CSP) method and the second consists of defining EEG spatial projection via regression. For this second step, a progressive orthogonalisation process is proposed to improve stability. This method has been tested on long-duration EEG recordings of epileptic patients. A neurologist quantified the ratio of removed artifacts and the ratio of preserved EEG. Among the 330 artifacted pages used for evaluation, readability was judged better for 78% of pages, equal for 20% of pages, and worse for 2%. Artifact amplitudes were reduced by 80% on average. At the same time, brain sources were preserved in amplitude from 70% to 95% depending on the type of waves (alpha, theta, delta, spikes, etc.). A blind comparison with manual Independent Component Analysis (ICA) was also realised. The results show that this method is competitive and useful for routine clinical practice.