497 research outputs found
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces
A major issue in Motor Imagery Brain-Computer Interfaces (MI-BCIs) is their
poor classification accuracy and the large amount of data that is required for
subject-specific calibration. This makes BCIs less accessible to general users
in out-of-the-lab applications. This study employed deep transfer learning for
development of calibration-free subject-independent MI-BCI classifiers. Unlike
earlier works that applied signal preprocessing and feature engineering steps
in transfer learning, this study adopted an end-to-end deep learning approach
on raw EEG signals. Three deep learning models (MIN2Net, EEGNet and
DeepConvNet) were trained and compared using an openly available dataset. The
dataset contained EEG signals from 55 subjects who conducted a left- vs.
right-hand motor imagery task. To evaluate the performance of each model, a
leave-one-subject-out cross validation was used. The results of the models
differed significantly. MIN2Net was not able to differentiate right- vs.
left-hand motor imagery of new users, with a median accuracy of 51.7%. The
other two models performed better, with median accuracies of 62.5% for EEGNet
and 59.2% for DeepConvNet. These accuracies do not reach the required threshold
of 70% needed for significant control, however, they are similar to the
accuracies of these models when tested on other datasets without transfer
learning
An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing
This paper presents an accurate and robust embedded motor-imagery
brain-computer interface (MI-BCI). The proposed novel model, based on EEGNet,
matches the requirements of memory footprint and computational resources of
low-power microcontroller units (MCUs), such as the ARM Cortex-M family.
Furthermore, the paper presents a set of methods, including temporal
downsampling, channel selection, and narrowing of the classification window, to
further scale down the model to relax memory requirements with negligible
accuracy degradation. Experimental results on the Physionet EEG Motor
Movement/Imagery Dataset show that standard EEGNet achieves 82.43%, 75.07%, and
65.07% classification accuracy on 2-, 3-, and 4-class MI tasks in global
validation, outperforming the state-of-the-art (SoA) convolutional neural
network (CNN) by 2.05%, 5.25%, and 5.48%. Our novel method further scales down
the standard EEGNet at a negligible accuracy loss of 0.31% with 7.6x memory
footprint reduction and a small accuracy loss of 2.51% with 15x reduction. The
scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and
consuming 4.28mJ per inference for operating the smallest model, and on a
Cortex-M7 with 44ms and 18.1mJ per inference for the medium-sized model,
enabling a fully autonomous, wearable, and accurate low-power BCI
The evolution of AI approaches for motor imagery EEG-based BCIs
The Motor Imagery (MI) electroencephalography (EEG) based Brain Computer
Interfaces (BCIs) allow the direct communication between humans and machines by
exploiting the neural pathways connected to motor imagination. Therefore, these
systems open the possibility of developing applications that could span from
the medical field to the entertainment industry. In this context, Artificial
Intelligence (AI) approaches become of fundamental importance especially when
wanting to provide a correct and coherent feedback to BCI users. Moreover,
publicly available datasets in the field of MI EEG-based BCIs have been widely
exploited to test new techniques from the AI domain. In this work, AI
approaches applied to datasets collected in different years and with different
devices but with coherent experimental paradigms are investigated with the aim
of providing a concise yet sufficiently comprehensive survey on the evolution
and influence of AI techniques on MI EEG-based BCI data.Comment: Submitted to Italian Workshop on Artificial Intelligence for Human
Machine Interaction (AIxHMI 2022), December 02, 2022, Udine, Ital
Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review
A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance
EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs
Universal Adversarial Perturbations for CNN Classifiers in EEG-Based BCIs
Multiple convolutional neural network (CNN) classifiers have been proposed
for electroencephalogram (EEG) based brain-computer interfaces (BCIs). However,
CNN models have been found vulnerable to universal adversarial perturbations
(UAPs), which are small and example-independent, yet powerful enough to degrade
the performance of a CNN model, when added to a benign example. This paper
proposes a novel total loss minimization (TLM) approach to generate UAPs for
EEG-based BCIs. Experimental results demonstrated the effectiveness of TLM on
three popular CNN classifiers for both target and non-target attacks. We also
verified the transferability of UAPs in EEG-based BCI systems. To our
knowledge, this is the first study on UAPs of CNN classifiers in EEG-based
BCIs. UAPs are easy to construct, and can attack BCIs in real-time, exposing a
potentially critical security concern of BCIs
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.
Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research
Shallow convolutional network excel for classifying motor imagery EEG in BCI applications
Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications
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