Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI

Mohamed, Abdul-Khaaliq; Marwala, Tshilidzi; John, L.R.

doi:10.1109/iembs.2011.6091552

Cited by 29 publications

(16 citation statements)

References 20 publications

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“…These features were chosen because of evidence for their value as features, for feature reduction or similar applications in BCIs or DOC (e.g. Hjorth parameters [17], brainrate [17], Wackermann features [17], power spectra [5], [17], coherence [18], [19], directed transfer function (DTF) [20], [21], approximate entropy [22], Shannon entropy [23], Bhattacharyya distance [24]) or in other fields of EEG-research (e.g. [25]).…”

Section: Introductionmentioning

confidence: 99%

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

et al. 2013

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Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

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Section: Introductionmentioning

confidence: 99%

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

et al. 2013

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“…Instead, our proposed work uses Gaussian membership function for each motor intention at every session whose parameters are statistically determined from the respective observations due to which it shows good performance with reduced computational load. In related works, Kayikcioglu and Aydemir in [40] have classified movement imagery signals using k-Nearest Neighbor (kNN) and Support Vector Machines in [25] have classified finger movements along with wrist movements using spectral features, reduced by Bhattacharyya distance using Mahalanobis distance based clustering and artificial neural network (ANN) where ANN outperforms the former. However, network classifiers have poor generalization i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Wrist [23][24][25][26] and finger movements [24][25][26] classified from EEG can aid in extending the functionality of prosthetic devices assisting handicapped individuals and open a new area of motor task related BCI research. Our previous work [26] has shown the discriminative capability of Extreme Energy Ratio (EER) based feature in decoding wrist and finger movements from EEG signal.…”

Section: Introductionmentioning

confidence: 99%

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Bhattacharyya

Pal

Konar

et al. 2015

Biomedical Signal Processing and Control

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a b s t r a c tFeature extraction and automatic classification of mental states is an interesting and open area of research in the field of brain-computer interfacing (BCI). A well-trained classifier would allow the BCI system to control an external assistive device in real world problems. Sometimes, standard existing classifiers fail to generalize the components of a non-stationary signal, like Electroencephalography (EEG) which may pose one or more problems during real-time usage of the BCI system. In this paper, we aim to tackle this issue by designing an interval type-2 fuzzy classifier which deals with the uncertainties of the EEG signal over various sessions. Our designed classifier is used to decode various movements concerning the wrist (extension and flexion) and finger (opening and closing of a fist). For this purpose, we have employed extreme energy ratio (EER) to construct the feature vector. The average classification accuracy achieved during offline training and online testing over eight subjects are 86.45% and 78.44%, respectively. On comparison with other related works, it is shown that our designed IT2FS classifier presents a better performance.

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“…As an extension of our previous research logistic regression is used as a classifier, and output of the classifier is used for generating command signals to control upper limb prosthesis [17–19]. …”

Section: Introductionmentioning

confidence: 99%

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

Rashid

Iqbal

Javed

et al. 2018

BioMed Research International

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Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement). Two-stage logistic regression classifier exhibited the highest classification accuracy while Power Spectral Density (PSD) was used as a feature of the filtered signal. The EEG signal data set was recorded using a 14-channel electrode headset (a noninvasive BCI system) from right-handed, neurologically intact volunteers. Mu (commonly known as alpha waves) and Beta Rhythms (8–30 Hz) containing most of the movement data were retained through filtering using “Arduino Uno” microcontroller followed by 2-stage logistic regression to obtain a mean classification accuracy of 70%.

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Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI

Cited by 29 publications

References 20 publications

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

Comparison of EEG-Features and Classification Methods for Motor Imagery in Patients with Disorders of Consciousness

An interval type-2 fuzzy approach for real-time EEG-based control of wrist and finger movement

Design of Embedded System for Multivariate Classification of Finger and Thumb Movements Using EEG Signals for Control of Upper Limb Prosthesis

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