SVM for Semi-automatic Selection of ICA Components of Electromyogenic Artifacts in EEG Data

Gabsteiger, Florian; Leutheuser, Heike; Reis, Pedro; Lochmann, Matthias; Eskofier, Bjoern M.

doi:10.1007/978-3-319-02913-9_34

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…However, to our knowledge, this software has not been specifically tested in EEG sports applications. In 2013, Gabsteiger and colleagues used an SVM classifier to automatically classify myogenic artifactual ICs in a study with specific neck and body movement exercises (Gabsteiger et al, 2014 ). These authors obtained good results when they evaluated their classifier in a test dataset from the same study (achieving 93% sensitivity and 96% specificity).…”

Section: Discussionmentioning

confidence: 99%

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Stone

Tamburro

Fiedler

et al. 2018

Front. Hum. Neurosci.

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Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications.

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Section: Discussionmentioning

confidence: 99%

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Stone

Tamburro

Fiedler

et al. 2018

Front. Hum. Neurosci.

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“…Users can opt by manual selection or automatic selection . Automatic selection with MARA (Winkler et al, 2011 ), http://www.user.tu-berlin.de/irene.winkler/artifacts/ or ACEMIC (Gabsteiger et al, 2013 ) available at http://www5.cs.fau.de/research/areas/digital-sports/automatic-classification-of-electromyogenic-ica-components/ . Criteria for the manual selection of EMG and other noise components are described in Goncharova et al ( 2003 ); McMenamin et al ( 2010 ).…”

Section: Data Analysis Software and Artifact Removal Techniquesmentioning

confidence: 99%

“…Thus, Gabsteiger et al ( 2013 ) trained a classifier for the selection of muscle activity independent components. It is designed to cover a diverse selection of exercises that stimulate the musculature that most interfere in EEG recordings during movement: the Automatic Classification of Electromyogenic ICA Components (ACEMIC).…”

Section: Data Analysis Software and Artifact Removal Techniquesmentioning

confidence: 99%

Methodological aspects of EEG and body dynamics measurements during motion

Reis¹,

Hebenstreit

Gabsteiger

et al. 2014

Front. Hum. Neurosci.

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EEG involves the recording, analysis, and interpretation of voltages recorded on the human scalp which originate from brain gray matter. EEG is one of the most popular methods of studying and understanding the processes that underlie behavior. This is so, because EEG is relatively cheap, easy to wear, light weight and has high temporal resolution. In terms of behavior, this encompasses actions, such as movements that are performed in response to the environment. However, there are methodological difficulties which can occur when recording EEG during movement such as movement artifacts. Thus, most studies about the human brain have examined activations during static conditions. This article attempts to compile and describe relevant methodological solutions that emerged in order to measure body and brain dynamics during motion. These descriptions cover suggestions on how to avoid and reduce motion artifacts, hardware, software and techniques for synchronously recording EEG, EMG, kinematics, kinetics, and eye movements during motion. Additionally, we present various recording systems, EEG electrodes, caps and methods for determinating real/custom electrode positions. In the end we will conclude that it is possible to record and analyze synchronized brain and body dynamics related to movement or exercise tasks.

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“…We therefore removed 5 out of 64 ICs in both cases to allow a fair comparison. We worked with an automatic classifier that was specifically designed to discriminate between electromyogenic and neural ICA components to reduce user-dependent factors [19].…”

Section: Ica Component Rejectionmentioning

confidence: 99%

ICA-based reduction of electromyogenic artifacts in EEG data: Comparison with and without EMG data

Gabsteiger

Leutheuser

Reis

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA). ICA estimates statistically independent components (ICs) that, when linearly combined, closely match the input (sensor) data. Removing the ICs that represent artifact sources and re-mixing the sources returns the input data with reduced noise activity. ICs of real-world data are usually not perfectly separated, actual sources, but a mixture of these sources. Adding additional input signals, predominantly generated by a single IC that is already part of the original sensor data, should increase that IC's separability. We conducted this study to evaluate this concept for ICA-based electromyogenic artifact reduction in EEG using EMG signals as additional inputs. To acquire the appropriate data we worked with nine human volunteers. The EEG and EMG were recorded while the study volunteers performed seven exercises designed to produce a wide range of representative myogenic artifacts. To evaluate the effect of the EMG signals we estimated the sources of each dataset once with and once without the EMG data. The ICs were automatically classified as either `myogenic' or `non-myogenic'. We removed the former before back projection. Afterwards we calculated an objective measure to quantify the artifact reduction and assess the effect of including EMG signals. Our study showed that the ICA-based reduction of electromyogenic artifacts can be improved by including the EMG data of artifact-inducing muscles. This approach could prove beneficial for locomotor disorder research, brain-computer interfaces, neurofeedback, and most other areas where brain activity during movement has to be analyzed.

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SVM for Semi-automatic Selection of ICA Components of Electromyogenic Artifacts in EEG Data

Cited by 5 publications

References 12 publications

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

Methodological aspects of EEG and body dynamics measurements during motion

ICA-based reduction of electromyogenic artifacts in EEG data: Comparison with and without EMG data

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