Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …
Motor Imagery Signal Classification using Adversarial Learning: A systematic literature review
S Mishra, O Mahmudi, A Jalali - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a comprehensive Systematic Literature Review (SLR) on the utilization
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method
Brain-computer interfaces (BCIs) enable communication between humans and machines by
translating brain activity into control commands. Electroencephalography (EEG) signals are …
translating brain activity into control commands. Electroencephalography (EEG) signals are …
Perturbing BEAMs: EEG adversarial attack to deep learning models for epilepsy diagnosing
J Yu, K Qiu, P Wang, C Su, Y Fan, Y Cao - BMC Medical Informatics and …, 2023 - Springer
Deep learning models have been widely used in electroencephalogram (EEG) analysis and
obtained excellent performance. But the adversarial attack and defense for them should be …
obtained excellent performance. But the adversarial attack and defense for them should be …
Revisiting multi-dimensional classification from a dimension-wise perspective
Real-world objects exhibit intricate semantic properties that can be characterized from a
multitude of perspectives, which necessitates the development of a model capable of …
multitude of perspectives, which necessitates the development of a model capable of …
Generative perturbation network for universal adversarial attacks on brain-computer interfaces
Deep neural networks (DNNs) have successfully classified EEG-based brain-computer
interface (BCI) systems. However, recent studies have found that well-designed input …
interface (BCI) systems. However, recent studies have found that well-designed input …
Single-Sensor Sparse Adversarial Perturbation Attacks Against Behavioural Biometrics
R Gunawardena, S Jayawardena… - IEEE Internet of …, 2024 - ieeexplore.ieee.org
In Internet of Things (IoT) deployments, sensing applications have emerged as critical tools.
They combine data streams from heterogeneous, untrusted sensors to provide valuable …
They combine data streams from heterogeneous, untrusted sensors to provide valuable …
Frequency Domain Channel-wise Attack to CNN Classifiers in Motor Imagery Brain-Computer Interfaces
Objective: Convolutional neural network (CNN), a classical structure in deep learning, has
been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many …
been commonly deployed in the motor imagery brain-computer interface (MIBCI). Many …
Effects of Un targeted Adversarial Attacks on Deep Learning Methods
The increasing connectivity of smart systems opens up new security vulnerabilities. Since
smart systems are used in various sectors such as healthcare, smart cities, and the …
smart systems are used in various sectors such as healthcare, smart cities, and the …
[HTML][HTML] Assessing robustness to adversarial attacks in attention-based networks: Case of EEG-based motor imagery classification
The classification of motor imagery (MI) using Electroencephalography (EEG) plays a pivotal
role in facilitating communication for individuals with physical limitations through Brain …
role in facilitating communication for individuals with physical limitations through Brain …