scholarly journals Brain Computer Interface-EEG based Imagined Word Prediction Using Convolutional Neural Network Visual Stimuli for Speech Disability

2022 ◽  
Author(s):  
Babu Chinta ◽  
Moorthi M
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Performance Metrics ◽  
Brain Computer Interface ◽  
Visual Stimuli ◽  
Learning Model ◽  
Computer Interface ◽  
Healthcare Sector ◽  
Word Prediction

Abstract Brain Computer Interface (BCI) is one of the fast-growing technological trends, which finds its applications in the field of the healthcare sector. In this work, 16 electrodes of Electroencephalography (EEG) placed according to the 10-20 electrode system are used to acquire the EEG signals. A BCI with EEG based imagined word prediction using Convolutional Neural Network (CNN) is modeled and trained to recognize the words imagined through the EEG brain signal, where the CNN model Alexnet and Googlenet are able to recognize the words due to visual stimuli namely, up, down, right, left and up to ten words. The performance metrics are improved with the Morlet Continuous wavelet transform applied at the pre-processing stage, with seven extracted features such as mean, standard deviation, skewness, kurtosis, bandpower, root mean square, and Shannon entropy. Based on the testing, Alexnet transfer learning model performed better as compared to Googlenet transfer learning model, as it achieved an accuracy of 90.3%, recall, precision, and F1 score of 91.4%, 90%, and 90.7% respectively for seven extracted features. However, the performance metrics decreased when the number of extracted features was reduced from seven to four, to 83.8%, 84.4%, 82.9%, and 83.6% respectively. This high accuracy further paves the way to future work on cross participant analysis, plan to involve a larger number of participants for testing and to enhance the deep learning neural networks to create the system developed to be suitable for EEG based mobile applications, which helps to identify what the words are imagined to be uttered by the speech-disabled persons.

scholarly journals Brain Computer Interface -EEG Based Imagined Word Prediction Using Convolutional Neural Network Visual Stimuli for Speech Disability

2021 ◽  
Author(s):  
Babu Chinta ◽  
Moorthi M
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Performance Metrics ◽  
Brain Computer Interface ◽  
Visual Stimuli ◽  
Learning Model ◽  
Computer Interface ◽  
Healthcare Sector ◽  
Word Prediction

Abstract Background: Brain Computer Interface (BCI) is one of the fast-growing technological trends, which finds its applications in the field of the healthcare sector. In this work, 16 electrodes of Electroencephalography (EEG) placed according to the 10-20 electrode system is used to acquire the EEG signals. A BCI with EEG based imagined word prediction using Convolutional Neural Network (CNN) is modeled and trained to recognize the words imagined through the EEG brain signal, where the CNN model Alexnet and Googlenet are able to recognize the words due to visual stimuli namely, up, down, right, left and up to ten words. The performance metrics are improved with the Morlet Continuous wavelet transform applied at the pre-processing stage, with seven extracted features such as mean, standard deviation, skewness, kurtosis, bandpower, root mean square, and Shannon entropy.Results: Based on the testing, Alexnet transfer learning model performed better as compared to Googlenet transfer learning model, as it achieved an accuracy of 90.3%, recall, precision, and F1 score of 91.4%, 90%, and 90.7% respectively for seven extracted features. However, the performance metrics decreased when the number of extracted features was reduced from seven to four, to 83.8%, 84.4%, 82.9%, and 83.6% respectively.Conclusions: The Alexnet transfer learning model is selected to be the best model as compared Googlenet, as it achieved an accuracy of 90.3% and the final training option of 80 epoch, 64 batch size, scalogram pre-processing method, ratio of 80:20 training and validation set and initial learning rate of 0.0001.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

scholarly journals Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1199 ◽  
Author(s):  
Hyeon Kyu Lee ◽  
Young-Seok Choi
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Continuous Wavelet Transform ◽  
Brain Computer Interface ◽  
Classification Performance ◽  
Computer Interface ◽  
Continuous Wavelet ◽  
Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

scholarly journals Classification of the gaze fixations in the eye-brain-computer interface paradigm with a compact convolutional neural network

2018 ◽  
Vol 145 ◽  
pp. 293-299
Author(s):  
Bogdan L. Kozyrskiy ◽  
Anastasia O. Ovchinnikova ◽  
Alena D. Moskalenko ◽  
Boris M. Velichkovsky ◽  
Sergei L. Shishkin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
The Gaze ◽  
Gaze Fixations

scholarly journals Spatial and Time Domain Feature of ERP Speller System Extracted via Convolutional Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jaehong Yoon ◽  
Jungnyun Lee ◽  
Mincheol Whang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Parietal Lobe ◽  
Brain Computer Interface ◽  
Occipital Lobe ◽  
Event Related Potential ◽  
Computer Interface ◽  
Feature Maps ◽  
Neural Activities ◽  
Temporal Features

Feature of event-related potential (ERP) has not been completely understood and illiteracy problem remains unsolved. To this end, P300 peak has been used as the feature of ERP in most brain–computer interface applications, but subjects who do not show such peak are common. Recent development of convolutional neural network provides a way to analyze spatial and temporal features of ERP. Here, we train the convolutional neural network with 2 convolutional layers whose feature maps represented spatial and temporal features of event-related potential. We have found that nonilliterate subjects’ ERP show high correlation between occipital lobe and parietal lobe, whereas illiterate subjects only show correlation between neural activities from frontal lobe and central lobe. The nonilliterates showed peaks in P300, P500, and P700, whereas illiterates mostly showed peaks in around P700. P700 was strong in both subjects. We found that P700 peak may be the key feature of ERP as it appears in both illiterate and nonilliterate subjects.

A novel approach of CNN for human motor imagery recognition using the virtual electrode pairs

2020 ◽  
pp. 1-14
Author(s):  
Xiangmin Lun ◽  
Zhenglin Yu ◽  
Fang Wang ◽  
Tao Chen ◽  
Yimin Hou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Single Subject ◽  
Test Model ◽  
Interface System ◽  
Virtual Electrodes ◽  
The Brain

In order to develop an efficient brain-computer interface system, the brain activity measured by electroencephalography needs to be accurately decoded. In this paper, a motor imagery classification approach is proposed, combining virtual electrodes on the cortex layer with a convolutional neural network; this can effectively improve the decoding performance of the brain-computer interface system. A three layer (cortex, skull, and scalp) head volume conduction model was established by using the symmetric boundary element method to map the scalp signal to the cortex area. Nine pairs of virtual electrodes were created on the cortex layer, and the features of the time and frequency sequence from the virtual electrodes were extracted by performing time-frequency analysis. Finally, the convolutional neural network was used to classify motor imagery tasks. The results show that the proposed approach is convergent in both the training model and the test model. Based on the Physionet motor imagery database, the averaged accuracy can reach 98.32% for a single subject, while the averaged values of accuracy, Kappa, precision, recall, and F1-score on the group-wise are 96.23%, 94.83%, 96.21%, 96.13%, and 96.14%, respectively. Based on the High Gamma database, the averaged accuracy has achieved 96.37% and 91.21% at the subject and group levels, respectively. Moreover, this approach is superior to those of other studies on the same database, which suggests robustness and adaptability to individual variability.

scholarly journals Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

10.2196/24762 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e24762
Author(s):  
Hyun-Lim Yang ◽  
Chul-Woo Jung ◽  
Seong Mi Yang ◽  
Min-Soo Kim ◽  
Sungho Shim ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Output ◽  
Deep Learning ◽  
Retrospective Study ◽  
Arterial Pressure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Model ◽  
Learning Technique ◽  
Deep Learning Model

Background Arterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. Objective In this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. Methods A retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. Results A total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P<.001). Conclusions The deep learning–based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care.

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