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      An EEG-based study of discrete isometric and isotonic human lower limb muscle contractions

      , 1 , 1

      Journal of NeuroEngineering and Rehabilitation

      BioMed Central

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          Abstract

          Background

          Electroencephalography (EEG) combined with independent component analysis enables functional neuroimaging in dynamic environments including during human locomotion. This type of functional neuroimaging could be a powerful tool for neurological rehabilitation. It could enable clinicians to monitor changes in motor control related cortical dynamics associated with a therapeutic intervention, and it could facilitate noninvasive electrocortical control of devices for assisting limb movement to stimulate activity dependent plasticity. Understanding the relationship between electrocortical dynamics and muscle activity will be helpful for incorporating EEG-based functional neuroimaging into clinical practice. The goal of this study was to use independent component analysis of high-density EEG to test whether we could relate electrocortical dynamics to lower limb muscle activation in a constrained motor task. A secondary goal was to assess the trial-by-trial consistency of the electrocortical dynamics by decoding the type of muscle action.

          Methods

          We recorded 264-channel EEG while 8 neurologically intact subjects performed isometric and isotonic, knee and ankle exercises at two different effort levels. Adaptive mixture independent component analysis (AMICA) parsed EEG into models of underlying source signals. We generated spectrograms for all electrocortical source signals and used a naïve Bayesian classifier to decode exercise type from trial-by-trial time-frequency data.

          Results

          AMICA captured different electrocortical source distributions for ankle and knee tasks. The fit of single-trial EEG to these models distinguished knee from ankle tasks with 80% accuracy. Electrocortical spectral modulations in the supplementary motor area were significantly different for isometric and isotonic tasks (p < 0.05). Isometric contractions elicited an event related desynchronization (ERD) in the α-band (8–12 Hz) and β-band (12–30 Hz) at joint torque onset and offset. Isotonic contractions elicited a sustained α- and β-band ERD throughout the trial. Classifiers based on supplementary motor area sources achieved a 4-way classification accuracy of 69% while classifiers based on electrocortical sources in multiple brain regions achieved a 4-way classification accuracy of 87%.

          Conclusions

          Independent component analysis of EEG reveals unique spatial and spectro-temporal electrocortical properties for different lower limb motor tasks. Using a broad distribution of electrocortical signals may improve classification of human lower limb movements from single-trial EEG.

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          Most cited references 34

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          Removing electroencephalographic artifacts by blind source separation.

          Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (EEG) interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic (EOG) recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis (PCA) has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis (ICA). Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.
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            Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources.

            An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions. This was achieved by using a simple type of learning rule first derived by Girolami (1997) by choosing negentropy as a projection pursuit index. Parameterized probability distributions that have sub- and supergaussian regimes were used to derive a general learning rule that preserves the simple architecture proposed by Bell and Sejnowski (1995), is optimized using the natural gradient by Amari (1998), and uses the stability analysis of Cardoso and Laheld (1996) to switch between sub- and supergaussian regimes. We demonstrate that the extended infomax algorithm is able to separate 20 sources with a variety of source distributions easily. Applied to high-dimensional data from electroencephalographic recordings, it is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
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              Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects.

              Electrical potentials produced by blinks and eye movements present serious problems for electroencephalographic (EEG) and event-related potential (ERP) data interpretation and analysis, particularly for analysis of data from some clinical populations. Often, all epochs contaminated by large eye artifacts are rejected as unusable, though this may prove unacceptable when blinks and eye movements occur frequently. Frontal channels are often used as reference signals to regress out eye artifacts, but inevitably portions of relevant EEG signals also appearing in EOG channels are thereby eliminated or mixed into other scalp channels. A generally applicable adaptive method for removing artifacts from EEG records based on blind source separation by independent component analysis (ICA) (Neural Computation 7 (1995) 1129; Neural Computation 10(8) (1998) 2103; Neural Computation 11(2) (1999) 606) overcomes these limitations. Results on EEG data collected from 28 normal controls and 22 clinical subjects performing a visual selective attention task show that ICA can be used to effectively detect, separate and remove ocular artifacts from even strongly contaminated EEG recordings. The results compare favorably to those obtained using rejection or regression methods. The ICA method can preserve ERP contributions from all of the recorded trials and all the recorded data channels, even when none of the single trials are artifact-free.
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                Author and article information

                Contributors
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central
                1743-0003
                2012
                9 June 2012
                : 9
                : 35
                Affiliations
                [1 ]Human Neuromechanics Laboratory, School of Kinesiology, University of Michigan, Ann Arbor, MI, USA
                Article
                1743-0003-9-35
                10.1186/1743-0003-9-35
                3476535
                22682644
                Copyright ©2012 Gwin and Ferris; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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                Research

                Neurosciences

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