High Accuracy and Short Delay 1ch-SSVEP Quadcopter-BMI Using Deep Learning

This study considers a brain-machine interface (BMI) system based on the steady state visually evoked potential (SSVEP) for controlling quadcopters using electroencephalography (EEG) signals. An EEG channel with a single dry electrode, i.e., without conductive gel or paste, was utilized to minimize the load on users. Convolutional neural network (CNN) and long short-term memory (LSTM) models, both of which have received significant research attention, were used to classify the EEG data obtained for flickers from multi-flicker screens at five different frequencies, with each flicker corresponding to a drone movement, viz., takeoff, forward and sideways movements, and landing. The subjects of the experiment were seven healthy men. Results indicate a high accuracy of 97% with the LSTM model for a 2 s segment used as the unit of processing. High accuracy of 93% for 0.5 s segment as a unit of processing can remain in the LSTM classification, consequently decreasing the delay of the system that may be required for safety reasons in real-time applications. A system demonstration was undertaken with 2 out of 7 subjects controlling the quadcopter and monitoring movements such as takeoff, forward motion, and landing, which showed a success rate of 90% on average.

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Classification of Noisy Epileptic EEG Signals Using Fortified Long Short-term Memory Network

Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology ◽

10.1145/3405758.3405782 ◽

2020 ◽

Author(s):

Jarno Vrolijk ◽

Maryam Alimardani

Keyword(s):

Short Term Memory ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Memory Network ◽

Long Short Term Memory

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A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2018.05.019 ◽

2018 ◽

Vol 99 ◽

pp. 24-37 ◽

Cited By ~ 93

Author(s):

Κostas Μ. Tsiouris ◽

Vasileios C. Pezoulas ◽

Michalis Zervakis ◽

Spiros Konitsiotis ◽

Dimitrios D. Koutsouris ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Epileptic Seizures ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Learning Network ◽

Long Short Term Memory ◽

Deep Learning Network

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Classification of Drowsiness Levels Based on a Deep Spatio-Temporal Convolutional Bidirectional LSTM Network Using Electroencephalography Signals

Brain Sciences ◽

10.3390/brainsci9120348 ◽

2019 ◽

Vol 9 (12) ◽

pp. 348 ◽

Cited By ~ 7

Author(s):

Ji-Hoon Jeong ◽

Baek-Woon Yu ◽

Dae-Hyeok Lee ◽

Seong-Whan Lee

Keyword(s):

Short Term Memory ◽

Mental States ◽

Critical Issue ◽

High Accuracy ◽

Classification Performance ◽

Eeg Signals ◽

Aviation Accidents ◽

Spatio Temporal ◽

Lstm Network

Non-invasive brain-computer interfaces (BCI) have been developed for recognizing human mental states with high accuracy and for decoding various types of mental conditions. In particular, accurately decoding a pilot’s mental state is a critical issue as more than 70% of aviation accidents are caused by human factors, such as fatigue or drowsiness. In this study, we report the classification of not only two mental states (i.e., alert and drowsy states) but also five drowsiness levels from electroencephalogram (EEG) signals. To the best of our knowledge, this approach is the first to classify drowsiness levels in detail using only EEG signals. We acquired EEG data from ten pilots in a simulated night flight environment. For accurate detection, we proposed a deep spatio-temporal convolutional bidirectional long short-term memory network (DSTCLN) model. We evaluated the classification performance using Karolinska sleepiness scale (KSS) values for two mental states and five drowsiness levels. The grand-averaged classification accuracies were 0.87 (±0.01) and 0.69 (±0.02), respectively. Hence, we demonstrated the feasibility of classifying five drowsiness levels with high accuracy using deep learning.

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A portable real-time EEG signal acquisition and tele-medicine system

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-204582 ◽

2020 ◽

pp. 1-11

Author(s):

Qiang Zhang ◽

Peng Wang ◽

Shanshan Li ◽

Yonghao Jing

Keyword(s):

Real Time ◽

Sql Server ◽

Signal Acquisition ◽

Eeg Signal ◽

Eeg Signals ◽

Eeg Data ◽

Sql Server Database ◽

High Power Consumption ◽

Machine Interface ◽

Sd Card

Since electroencephalogram (EEG) signals contain a variety of physiological and pathological information, they are widely used in medical diagnosis, brain machine interface and other fields. The existing EEG apparatus are not perfect due to big size, high power consumption and using cables to transmit data. In this paper, a portable real-time EEG signal acquisition and tele-medicine system is developed in order to improve performance of EEG apparatus. The weak EEG signals are induced to the pre-processing circuits via a noninvasive method with bipolar leads. After multi-level amplifying and filtering, these signals are transmitted to DSP (TMS320C5509) to conduct digital filtering. Then, the EEG signals are displayed on the LCD screen and stored in the SD card so that they can be uploaded to the server through the internet. The server employs SQL Server database to manage patients’ information and to store data in disk. Doctors can download, look up and analyze patients’ EEG data using the doctor client. Experimental results demonstrate that the system can acquire weak EEG signals in real time, display the processed results, save data and carry out tele-medicine. The system can meet the requirement of the EEG signals’ quality, and are easy to use and carry.

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Confusion detection using cognitive ability tests

The International FLAIRS Conference Proceedings ◽

10.32473/flairs.v34i1.128474 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Caroline Dakoure ◽

Mohamed Sahbi Benlamine ◽

Claude Frasson

Keyword(s):

Cognitive Ability ◽

Short Term Memory ◽

Nearest Neighbors ◽

Support Vector ◽

Eeg Signals ◽

Short Term ◽

K Nearest Neighbors ◽

Cognitive Ability Tests ◽

Ability Tests ◽

Long Short Term Memory

It is of great importance to detect users’ confusion in a variety of situations such as orientation, reasoning, learning, and memorization. Confusion affects our ability to make decisions and can lower our cognitive ability. This study examines whether a confusion recognition model based on EEG features, recorded on cognitive ability tests, can be used to detect three levels (low, medium, high) of confusion. This study also addresses the extraction of additional features relevant to classification. We compare the performance of the K-nearest neighbors (KNN), support vector memory (SVM), and long short-term memory (LSTM) models. Results suggest that confusion can be efficiently recognized with EEG signals (78.6% accuracy in detecting a confused/unconfused state and 68.0% accuracy in predicting the level of confusion). Implications for educational situations are discussed.

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Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation

Sensors ◽

10.3390/s18103226 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3226 ◽

Cited By ~ 13

Author(s):

Lingfeng Xu ◽

Xiang Chen ◽

Shuai Cao ◽

Xu Zhang ◽

Xun Chen

Keyword(s):

Neural Networks ◽

Muscle Force ◽

Short Term Memory ◽

Elbow Flexion ◽

High Accuracy ◽

Force Estimation ◽

Short Term ◽

Long Short Term Memory ◽

Lstm Network ◽

Time Subject

To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.

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CONVOLUTIONAL LONG-SHORT TERM MEMORY NETWORKS MODEL FOR LONG DURATION EEG SIGNAL CLASSIFICATION

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519419400050 ◽

2019 ◽

Vol 19 (01) ◽

pp. 1940005 ◽

Cited By ~ 3

Author(s):

ULAS BARAN BALOGLU ◽

ÖZAL YILDIRIM

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Algorithms ◽

Eeg Signals ◽

Short Term ◽

Term Memory ◽

Long Duration ◽

Proposed Model ◽

Clinical Scenarios ◽

Long Short Term Memory

Background and objective: Deep learning structures have recently achieved remarkable success in the field of machine learning. Convolutional neural networks (CNN) in image processing and long-short term memory (LSTM) in the time-series analysis are commonly used deep learning algorithms. Healthcare applications of deep learning algorithms provide important contributions for computer-aided diagnosis research. In this study, convolutional long-short term memory (CLSTM) network was used for automatic classification of EEG signals and automatic seizure detection. Methods: A new nine-layer deep network model consisting of convolutional and LSTM layers was designed. The signals processed in the convolutional layers were given as an input to the LSTM network whose outputs were processed in densely connected neural network layers. The EEG data is appropriate for a model having 1-D convolution layers. A bidirectional model was employed in the LSTM layer. Results: Bonn University EEG database with five different datasets was used for experimental studies. In this database, each dataset contains 23.6[Formula: see text]s duration 100 single channel EEG segments which consist of 4097 dimensional samples (173.61[Formula: see text]Hz). Eight two-class and three three-class clinical scenarios were examined. When the experimental results were evaluated, it was seen that the proposed model had high accuracy on both binary and ternary classification tasks. Conclusions: The proposed end-to-end learning structure showed a good performance without using any hand-crafted feature extraction or shallow classifiers to detect the seizures. The model does not require filtering, and also automatically learns to filter the input as well. As a result, the proposed model can process long duration EEG signals without applying segmentation, and can detect epileptic seizures automatically by using the correlation of ictal and interictal signals of raw data.

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Time series analysis and long short-term memory (LSTM) network prediction of BPV current density

Energy & Environmental Science ◽

10.1039/d0ee02970j ◽

2021 ◽

Vol 14 (4) ◽

pp. 2408-2418 ◽

Cited By ~ 1

Author(s):

Tonny I. Okedi ◽

Adrian C. Fisher

Keyword(s):

Time Series ◽

Current Density ◽

Short Term Memory ◽

High Accuracy ◽

Seasonal Component ◽

Short Term ◽

Term Memory ◽

Network Prediction ◽

Long Short Term Memory ◽

Lstm Network

LSTM networks are shown to predict the seasonal component of biophotovoltaic current density and photoresponse to high accuracy.

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Weather Forecasting Using Merged Long Short-term Memory Model

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v7i3.1181 ◽

2018 ◽

Vol 7 (3) ◽

pp. 377-385 ◽

Cited By ~ 1

Author(s):

Afan Galih Salman ◽

Yaya Heryadi ◽

Edi Abdurahman ◽

Wayan Suparta

Keyword(s):

Short Term Memory ◽

Weather Forecasting ◽

Human Life ◽

Predictor Variable ◽

Dew Point ◽

Memory Model ◽

Short Term ◽

Research Attention ◽

Term Memory ◽

Long Short Term Memory

Over decades, weather forecasting has attracted researchers from worldwide communities due to itssignificant effect to global human life ranging from agriculture, air trafic control to public security. Although formal study on weather forecasting has been started since 19th century, research attention to weather forecasting tasks increased significantly after weather big data are widely available. This paper proposed merged-Long Short-term Memory for forecasting ground visibility at the airpot using timeseries of predictor variable combined with another variable as moderating variable. The proposed models were tested using weather timeseries data at Hang Nadim Airport, Batam. The experiment results showedthe best average accuracy for forecasting visibility using merged Long Short-term Memory model and temperature and dew point as a moderating variable was (88.6%); whilst, using basic Long Short-term Memory without moderating variablewasonly (83.8%) respectively (increased by 4.8%).

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