Neuro-feedback system for real-time BCI decision prediction

Bhattacharyya, Sumanta; Das, Swatilekha; Das, Arijit; Dey, Rajesh; Dhar, Rudra Sankar

doi:10.1007/s00542-020-05146-4

Cited by 9 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data-driven multivariate methods and machine learning methods can play a role in the analyses of multi-modal brain imaging data. Real-time implementation : highly desired in practical applications such as brain-computer interfaces and neurofeedback [ 298 , 299 ]. Real-time applications are very challenging tasks, mostly due to the nature of these signals, however, some promising studies can be seen in: [ 291 , 298 , 300 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

View full text Add to dashboard Cite

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Real-time implementation: highly desired in practical applications such as brain-computer interfaces and neurofeedback [298,299]. Real-time applications are very challenging tasks, mostly due to the nature of these signals, however, some promising studies can be seen in: [291,298,300].…”

mentioning

confidence: 99%

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Martínek

Ládrová

Šidiková

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…4,76) Along with real-time EEG monitoring, EEG source localization can be applied to the braincomputer interface, which is implemented by analyzing signals obtained from the brain and transmitting them to a computer. [77][78][79] Although many challenges require addressing, EEG source localization is expected to play an important role in brain signal analyses. Numerous neuroimaging studies are currently underway and data on EEG localization being published, but the data from these studies requires unification and integration into big data in the future, which requires the standardization of research protocols and measurement methods.…”

Section: Future Directionsmentioning

confidence: 99%

“…EEG source localization is increasingly expanding in application and continues to evolveevolve ( Table 2 ) [ 70 - 77 ]. Multimodal methods, such as the integration of the high temporal resolution of EEG and high spatial resolution of fMRI, can be a promising approach.…”

Section: Future Directionsmentioning

confidence: 99%

Electroencephalography source localization

Eom¹

2023

Clin Exp Pediatr

View full text Add to dashboard Cite

Electroencephalography (EEG) has been and is still widely used in brain function research.EEG has advantages over other neuroimaging modalities. First, it not only directly images the electrical activity of neurons; it has a higher temporal resolution. Furthermore, current advanced technologies enable accurate mathematical calculations and sophisticated localization from EEG data. Several important factors should be considered for EEG analysis using these advanced technologies. First, raw EEG data contain physiological or nonphysiological artifacts. Therefore, preprocessing methods and algorithms to detect and remove these artifacts have been proposed and developed. In the analysis of preprocessed EEG, forward and inverse problems require solving and several proposed models have been applied. To solve the forward problem, the source information and matrix parameters from which the EEG originates are essential. Therefore, an accurate head model is required. In contrast, the possible combinations of the current sources computed inversely from EEG measured at a limited number of electrodes are infinite, referring to the inverse problem. The inverse problem can be solved by setting limits based on assumptions made of the anatomy and physiology on the generation and propagation of the current sources. Thus, methods such as dipole source models and distributed source models have been proposed. Source localization requires the consideration of many factors such as the preprocessing of raw EEG data, artifact removal, accurate head models and forward problems, and inverse computation problems. This review summarizes the methods and considerations applied to the above EEG source localization process. It also introduces the applications of EEG source localization for epilepsy and other diseases as well as brain function studies and discusses future directions.

show abstract

“…MI-BCI can help some disabled patients independently control external devices such as wheelchairs (Wang and Bezerianos, 2017 ) and artificial limbs (Condori et al, 2016 ; Cho et al, 2019 ). Bhattacharyya et al ( 2021 ) designed a real-time BCI neurofeedback system to reflect the expected tasks of hand movement and imagery.…”

Section: Introductionmentioning

confidence: 99%

A Motor Imagery Signals Classification Method via the Difference of EEG Signals Between Left and Right Hemispheric Electrodes

et al. 2022

View full text Add to dashboard Cite

Brain-computer interface (BCI) based on motor imagery (MI) can help patients with limb movement disorders in their normal life. In order to develop an efficient BCI system, it is necessary to decode high-accuracy motion intention by electroencephalogram (EEG) with low signal-to-noise ratio. In this article, a MI classification approach is proposed, combining the difference in EEG signals between the left and right hemispheric electrodes with a dual convolutional neural network (dual-CNN), which effectively improved the decoding performance of BCI. The positive and inverse problems of EEG were solved by the boundary element method (BEM) and weighted minimum norm estimation (WMNE), and then the scalp signals were mapped to the cortex layer. We created nine pairs of new electrodes on the cortex as the region of interest. The time series of the nine electrodes on the left and right hemispheric are respectively used as the input of the dual-CNN model to classify four MI tasks. The results show that this method has good results in both group-level subjects and individual subjects. On the Physionet database, the averaged accuracy on group-level can reach 96.36%, while the accuracies of four MI tasks reach 98.54, 95.02, 93.66, and 96.19%, respectively. As for the individual subject, the highest accuracy is 98.88%, and its four MI accuracies are 99.62, 99.68, 98.47, and 97.73%, respectively.

show abstract

Neuro-feedback system for real-time BCI decision prediction

Cited by 9 publications

References 22 publications

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Electroencephalography source localization

A Motor Imagery Signals Classification Method via the Difference of EEG Signals Between Left and Right Hemispheric Electrodes

Contact Info

Product

Resources

About