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      Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

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          Abstract

          Background

          For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks.

          Methods

          12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O 2Hb] signal features (Δ[O 2Hb] mean signal amplitudes, variance, skewness and kurtosis).

          Results

          The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O 2Hb] signal features comprising Δ[O 2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%.

          Conclusions

          Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation.

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          Most cited references29

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          Cerebral location of international 10-20 system electrode placement.

          We employed CT scanning to correlate scalp markers placed according to the international 10-20 system with underlying cerebral structures. Subjects were 12 normal volunteers. Measurements included assessment for cranial asymmetry to determine the effect of skull asymmetry on cortical location of electrodes. Results were correlated with the cortical histological map of Brodmann. Primary cortical locations agree well with previously published data and provide cortical localization in greater detail than previous studies. Variability of cortical electrode location was substantial in some cases and not related to cranial asymmetry. The results indicate that CT scanning or other neuroimaging techniques which reveal detailed cerebral anatomy would be potentially highly useful in defining the generators of electrocerebral potentials recorded from the scalp.
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            Motor imagery: a backdoor to the motor system after stroke?

            Understanding brain plasticity after stroke is important in developing rehabilitation strategies. Active movement therapies show considerable promise but depend on motor performance, excluding many otherwise eligible patients. Motor imagery is widely used in sport to improve performance, which raises the possibility of applying it both as a rehabilitation method and to access the motor network independently of recovery. Specifically, whether the primary motor cortex (M1), considered a prime target of poststroke rehabilitation, is involved in motor imagery is unresolved. We review methodological considerations when applying motor imagery to healthy subjects and in patients with stroke, which may disrupt the motor imagery network. We then review firstly the motor imagery training literature focusing on upper-limb recovery, and secondly the functional imaging literature in healthy subjects and in patients with stroke. The review highlights the difficulty in addressing cognitive screening and compliance in motor imagery studies, particularly with regards to patients with stroke. Despite this, the literature suggests the encouraging effect of motor imagery training on motor recovery after stroke. Based on the available literature in healthy volunteers, robust activation of the nonprimary motor structures, but only weak and inconsistent activation of M1, occurs during motor imagery. In patients with stroke, the cortical activation patterns are essentially unexplored as is the underlying mechanism of motor imagery training. Provided appropriate methodology is implemented, motor imagery may provide a valuable tool to access the motor network and improve outcome after stroke.
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              Principles derived from the study of simple skills do not generalize to complex skill learning.

              We review research related to the learning of complex motor skills with respect to principles developed on the basis of simple skill learning. Although some factors seem to have opposite effects on the learning of simple and of complex skills, other factors appear to be relevant mainly for the learning of more complex skills. We interpret these apparently contradictory findings as suggesting that situations with low processing demands benefit from practice conditions that increase the load and challenge the performer, whereas practice conditions that result in extremely high load should benefit from conditions that reduce the load to more manageable levels. The findings reviewed here call into question the generalizability of results from studies using simple laboratory tasks to the learning of complex motor skills. They also demonstrate the need to use more complex skills in motor-learning research in order to gain further insights into the learning process.
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                Author and article information

                Journal
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central
                1743-0003
                2011
                18 June 2011
                : 8
                : 34
                Affiliations
                [1 ]Biomedical Optics Research Laboratory (BORL), Division of Neonatology, Department of Obstetrics and Gynecology, University Hospital Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
                [2 ]Institute of Neuroinformatics (INI), University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
                Article
                1743-0003-8-34
                10.1186/1743-0003-8-34
                3133548
                21682906
                e6b8bc40-abaf-4960-ae92-422d107a8e5f
                Copyright ©2011 Holper and Wolf; 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.

                History
                : 14 December 2010
                : 18 June 2011
                Categories
                Research

                Neurosciences
                linear discriminant analysis,motor imagery,single-trial classification,wireless functional near-infrared spectroscopy (fnirs),brain computer interface (bci),motor execution

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