scholarly journals A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

2021 ◽  
Vol 15 ◽  
Author(s):  
Wonjun Ko ◽  
Eunjin Jeon ◽  
Seungwoo Jeong ◽  
Jaeun Phyo ◽  
Heung-Il Suk
Keyword(s):  
Deep Learning ◽  
Explicit Knowledge ◽  
Data Augmentation ◽  
Generative Model ◽  
Machine Learning Techniques ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Training Samples ◽  
Calibration Methods ◽  
Complex Patterns

Brain–computer interfaces (BCIs) utilizing machine learning techniques are an emerging technology that enables a communication pathway between a user and an external system, such as a computer. Owing to its practicality, electroencephalography (EEG) is one of the most widely used measurements for BCI. However, EEG has complex patterns and EEG-based BCIs mostly involve a cost/time-consuming calibration phase; thus, acquiring sufficient EEG data is rarely possible. Recently, deep learning (DL) has had a theoretical/practical impact on BCI research because of its use in learning representations of complex patterns inherent in EEG. Moreover, algorithmic advances in DL facilitate short/zero-calibration in BCI, thereby suppressing the data acquisition phase. Those advancements include data augmentation (DA), increasing the number of training samples without acquiring additional data, and transfer learning (TL), taking advantage of representative knowledge obtained from one dataset to address the so-called data insufficiency problem in other datasets. In this study, we review DL-based short/zero-calibration methods for BCI. Further, we elaborate methodological/algorithmic trends, highlight intriguing approaches in the literature, and discuss directions for further research. In particular, we search for generative model-based and geometric manipulation-based DA methods. Additionally, we categorize TL techniques in DL-based BCIs into explicit and implicit methods. Our systematization reveals advances in the DA and TL methods. Among the studies reviewed herein, ~45% of DA studies used generative model-based techniques, whereas ~45% of TL studies used explicit knowledge transferring strategy. Moreover, based on our literature review, we recommend an appropriate DA strategy for DL-based BCIs and discuss trends of TLs used in DL-based BCIs.

scholarly journals Spiking Neural Network for Augmenting Electroencephalographic Data for Brain Computer Interfaces

2021 ◽  
Vol 15 ◽  
Author(s):  
Sai Kalyan Ranga Singanamalla ◽  
Chin-Teng Lin
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Augmentation ◽  
Training Session ◽  
Original Data ◽  
Imaging Method ◽  
Spiking Neural Network ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Computer Interfaces

With the advent of advanced machine learning methods, the performance of brain–computer interfaces (BCIs) has improved unprecedentedly. However, electroencephalography (EEG), a commonly used brain imaging method for BCI, is characterized by a tedious experimental setup, frequent data loss due to artifacts, and is time consuming for bulk trial recordings to take advantage of the capabilities of deep learning classifiers. Some studies have tried to address this issue by generating artificial EEG signals. However, a few of these methods are limited in retaining the prominent features or biomarker of the signal. And, other deep learning-based generative methods require a huge number of samples for training, and a majority of these models can handle data augmentation of one category or class of data at any training session. Therefore, there exists a necessity for a generative model that can generate synthetic EEG samples with as few available trials as possible and generate multi-class while retaining the biomarker of the signal. Since EEG signal represents an accumulation of action potentials from neuronal populations beneath the scalp surface and as spiking neural network (SNN), a biologically closer artificial neural network, communicates via spiking behavior, we propose an SNN-based approach using surrogate-gradient descent learning to reconstruct and generate multi-class artificial EEG signals from just a few original samples. The network was employed for augmenting motor imagery (MI) and steady-state visually evoked potential (SSVEP) data. These artificial data are further validated through classification and correlation metrics to assess its resemblance with original data and in-turn enhanced the MI classification performance.

scholarly journals A Data Augmentation Scheme for Geometric Deep Learning in Personalized Brain–Computer Interfaces

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 162218-162229
Author(s):  
Fotis P. Kalaganis ◽  
Nikolaos A. Laskaris ◽  
Elisavet Chatzilari ◽  
Spiros Nikolopoulos ◽  
Ioannis Kompatsiaris
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Viruses ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 769 ◽  
Author(s):  
Ahmed Sedik ◽  
Abdullah M Iliyasu ◽  
Basma Abd El-Rahiem ◽  
Mohammed E. Abdel Samea ◽  
Asmaa Abdel-Raheem ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
Testing Time ◽  
Learning Models ◽  
Accurate Identification ◽  
Infected People ◽  
Corona Virus

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

scholarly journals Bigger data for big data: From Twitter to brain–computer interfaces

2014 ◽  
Vol 37 (1) ◽  
pp. 97-98 ◽  
Author(s):  
Etienne B. Roesch ◽  
Frederic Stahl ◽  
Mohamed Medhat Gaber
Keyword(s):  
Big Data ◽  
Generative Model ◽  
Wisdom Of Crowds ◽  
Brain Computer Interfaces ◽  
Sources Of Information ◽  
Computer Interfaces ◽  
Successful Attempt ◽  
Technical Features ◽  
Indirect Information ◽  
The Wisdom Of Crowds

AbstractWe are sympathetic with Bentley et al.’s attempt to encompass the wisdom of crowds in a generative model, but posit that a successful attempt at using big data will include more sensitive measurements, more varied sources of information, and will also build from the indirect information available through technology, from ancillary technical features to data from brain–computer interfaces.

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