Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Ahmed, Taufique; Longo, Luca

doi:10.1109/access.2022.3212777

Cited by 13 publications

(10 citation statements)

References 40 publications

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“…The VAE represents a generative model that integrates a deep neural network with a probabilistic framework [ 36 ]. It consists of an encoder network responsible for transforming input data into a latent space.…”

Section: Proposed Modelmentioning

confidence: 99%

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

Bhatti,

Choi

2024

Sensors

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The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.

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Section: Proposed Modelmentioning

confidence: 99%

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

Bhatti,

Choi

2024

Sensors

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“…In recent years, deep learning methods have been developed to detect various neurological disorders that utilize EEG signals [15], [75] and solve various classification tasks [76], [77], [78]. Although these methods work well in finding hidden features and patterns from the nonlinear data, they struggle to attain higher-classification accuracy on EEG due to the data being highly complex and the frequent non-cerebral contamination that accompanies it [61], [62].…”

Section: B Motivationmentioning

confidence: 99%

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

Grover

Chharia

Upadhyay

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuropsychiatric patients from healthy subjects. The study presents Schizo-Net, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the first which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. Schizo-Net surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.

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“…Spatial-domain analysis is used to examine the potential distribution of EEG signals at different scalp locations. Topographic maps [8] display the potential distribution of EEG signals on the scalp, thereby exploring the activity patterns and interactions of different brain regions. Source analysis aims to identify the active areas in the brain, which is crucial for locating brain lesions or studying brain functional connectivity.…”

Section: Introductionmentioning

confidence: 99%

Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data

He,

Chen,

et al. 2024

Biology

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Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.

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Examining the Size of the Latent Space of Convolutional Variational Autoencoders Trained With Spectral Topographic Maps of EEG Frequency Bands

Cited by 13 publications

References 40 publications

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

Enhancing IoT Healthcare with Federated Learning and Variational Autoencoder

Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

Unified Convolutional Sparse Transformer for Disease Diagnosis, Monitoring, Drug Development, and Therapeutic Effect Prediction from EEG Raw Data

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