Time Domain Features for EEG Signal Classification of Four Class Motor Imagery Using Artificial Neural Network

Widadi, Rahmat; Zulherman, Dodi; Ari, S. Rama Febriyan

doi:10.1007/978-981-33-6926-9_53

Cited by 4 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, there is a lack of EEG studies investigating tinnitus treatment. Compared with previous works using time-domain features as the object of analysis [ 40 , 41 , 42 , 43 ], we use frequency-domain features and functional-connectivity features to identify signal changes or patterns with higher feasibility, rather than just observing a single time domain information. As time-domain features depend on the nature of the signal, using frequency-domain features and functional-connectivity features provides stronger discriminative power.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Doborjeh

Liu

Doborjeh

et al. 2023

Sensors

View full text Add to dashboard Cite

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients’ responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients’ EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients’ outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%–100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

show abstract

Section: Discussionmentioning

confidence: 99%

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Doborjeh

Liu

Doborjeh

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The backpropagation algorithm is employed updating weights to ameliorate the network until achieving the pure output weight [44,46]. In this study, we use a Multi-Layered Perceptron (MLP) which is a feedforward artificial neural network (ANN) [44,47].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Miah

Muhammod

Mamun

et al. 2021

Journal of Neuroscience Methods

View full text Add to dashboard Cite

“…One practical approach is subject-specific models, as each individual may have unique self-regulation evoked responses in diverse frequency bands [ 14 ]. As a result, most MI-BCI systems are based on subject-specific temporal and spectral features [ 15 , 16 ], typically calculated on a single-trial basis. One example is the Filter Bank-Common Spatial Patterns (FBCSP) method, which leverages task-related brain rhythms primarily localized in the sensorimotor area [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

García-Murillo

Álvarez-Meza

Castellanos-Domínguez

2023

Diagnostics

View full text Add to dashboard Cite

This paper uses EEG data to introduce an approach for classifying right and left-hand classes in Motor Imagery (MI) tasks. The Kernel Cross-Spectral Functional Connectivity Network (KCS-FCnet) method addresses these limitations by providing richer spatial-temporal-spectral feature maps, a simpler architecture, and a more interpretable approach for EEG-driven MI discrimination. In particular, KCS-FCnet uses a single 1D-convolutional-based neural network to extract temporal-frequency features from raw EEG data and a cross-spectral Gaussian kernel connectivity layer to model channel functional relationships. As a result, the functional connectivity feature map reduces the number of parameters, improving interpretability by extracting meaningful patterns related to MI tasks. These patterns can be adapted to the subject’s unique characteristics. The validation results prove that introducing KCS-FCnet shallow architecture is a promising approach for EEG-based MI classification with the potential for real-world use in brain–computer interface systems.

show abstract

Time Domain Features for EEG Signal Classification of Four Class Motor Imagery Using Artificial Neural Network

Cited by 4 publications

References 11 publications

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

KCS-FCnet: Kernel Cross-Spectral Functional Connectivity Network for EEG-Based Motor Imagery Classification

Contact Info

Product

Resources

About