Signal processing techniques for motor imagery brain computer interface: A review

Aggarwal, Swati; Chugh, Nupur

doi:10.1016/j.array.2019.100003

Cited by 157 publications

(126 citation statements)

References 39 publications

Supporting

Mentioning

125

Contrasting

Order By: Relevance

“…A great deal of current studies, aiming to perfect BCI related techniques, focus on optimising aspects of the decoding algorithms (for example, Al-Saegh, Dawwd, and Abdul-Jabbar 2020; Roy et al 2019). Many sophisticated methods (Aggarwal and Chugh, 2019) have already been tried with limited success in surpassing a seemingly fixed plateau, determined by the inherently limiting signal to noise ratio of the EEG signal. On the other hand, approaches that try to instruct and incentivise the users to learn how to self-modulate their neural patterns towards increased separability, i.e.…”

Section: Discussionmentioning

confidence: 99%

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Mihelj

Bächinger

Kikkert

et al. 2021

Preprint

View full text Add to dashboard Cite

Neurofeedback (NF) in combination with motor imagery (MI) can be used for training individuals to volitionally modulate sensorimotor activity without producing overt movements. However, until now, NF methods were of limited utility for mentally training specific hand and finger actions. Here we employed a novel transcranial magnetic stimulation (TMS) based protocol to probe and detect MI-induced motor activity patterns in the primary motor cortex (M1) with the aim to reinforce selective facilitation of single finger representations. We showed that TMS-NF training but not MI training with uninformative feedback enabled participants to selectively upregulate corticomotor excitability of one finger, while simultaneously downregulating excitability of other finger representations within the same hand. Successful finger individuation during MI was accompanied by strong desynchronisation of sensorimotor brain rhythms, particularly in the beta band, as measured by electroencephalography. Additionally, informative TMS-NF promoted more dissociable EEG activation patterns underlying single finger MI, when compared to MI of the control group where no such feedback was provided. Our findings suggest that selective TMS-NF is a new approach for acquiring the ability of finger individuation even if no overt movements are performed. This might offer new treatment modality for rehabilitation after stroke or spinal cord injury.

show abstract

Section: Discussionmentioning

confidence: 99%

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Mihelj

Bächinger

Kikkert

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies that used this dataset, have mostly considered the [3 − 6] s window as the interval during which motor imagery tasks are performed [37, 42, 23, 25, 52, 69, 72] for the analysis. Therefore, we also extracted this 3-s motor imagery interval from each trial.…”

Section: Methodsmentioning

confidence: 99%

Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

Shamsi

Haddad

Najafizadeh

2020

Preprint

View full text Add to dashboard Cite

ObjectiveClassification of electroencephalography (EEG) signals with high accuracy using short recording intervals has been a challenging problem in developing brain computer interfaces (BCIs). This paper presents a novel feature extraction method for EEG recordings to tackle this problem.ApproachThe proposed approach is based on the concept that the brain functions in a dynamic manner, and utilizes dynamic functional connectivity graphs. The EEG data is first segmented into intervals during which functional networks sustain their connectivity. Functional connectivity networks for each identified segment are then localized, and graphs are constructed, which will be used as features. To take advantage of the dynamic nature of the generated graphs, a Long Short Term Memory (LSTM) classifier is employed for classificationMain resultsFeatures extracted from various durations of post-stimulus EEG data associated with motor execution and imagery tasks are used to test the performance of the classifier. Results show an average accuracy of 85.32% using features extracted from only 500 ms of the post-stimulus data.SignificanceOur results demonstrate, for the first time, that using the proposed feature extraction method, it is possible to classify motor tasks from EEG recordings using only few hundreds of milliseconds of the data. This duration is considerably shorter than what has been reported before. These results will have significant implications for improving the effectiveness and the speed of BCIs, particularly for those used in assistive technologies.

show abstract

“…The purpose of MI-BCI is to identify the imagined movements by classifying the electroencephalogram (EEG) characteristics of the brain, to control the external devices, such as robots [ 3 , 4 ]. On the one hand, MI-BCI can help patients with severe dysfunction and establish communication channels with the outside world.…”

Section: Introductionmentioning

confidence: 99%

A Novel Time‐Incremental End‐to‐End Shared Neural Network with Attention‐Based Feature Fusion for Multiclass Motor Imagery Recognition

Lian

Zuo

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

In the research of motor imagery brain-computer interface (MI-BCI), traditional electroencephalogram (EEG) signal recognition algorithms appear to be inefficient in extracting EEG signal features and improving classification accuracy. In this paper, we discuss a solution to this problem based on a novel step-by-step method of feature extraction and pattern classification for multiclass MI-EEG signals. First, the training data from all subjects is merged and enlarged through autoencoder to meet the need for massive amounts of data while reducing the bad effect on signal recognition because of randomness, instability, and individual variability of EEG data. Second, an end-to-end sharing structure with attention-based time-incremental shallow convolution neural network is proposed. Shallow convolution neural network (SCNN) and bidirectional long short-term memory (BiLSTM) network are used to extract frequency-spatial domain features and time-series features of EEG signals, respectively. Then, the attention model is introduced into the feature fusion layer to dynamically weight these extracted temporal-frequency-spatial domain features, which greatly contributes to the reduction of feature redundancy and the improvement of classification accuracy. At last, validation tests using BCI Competition IV 2a data sets show that classification accuracy and kappa coefficient have reached 82.7 ± 5.57% and 0.78 ± 0.074, which can strongly prove its advantages in improving classification accuracy and reducing individual difference among different subjects from the same network.

show abstract

Signal processing techniques for motor imagery brain computer interface: A review

Cited by 157 publications

References 39 publications

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback

Early Classification of Motor Tasks Using Dynamic Functional Connectivity Graphs from EEG

A Novel Time‐Incremental End‐to‐End Shared Neural Network with Attention‐Based Feature Fusion for Multiclass Motor Imagery Recognition

Contact Info

Product

Resources

About