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21 hours ago · In this study, we propose an ensemble learning framework for electroencephalogram-based overt speech classification, leveraging denoising diffusion ...
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21 hours ago · This study examined the performance of deep learning models for EEG-based neural decoding, focusing on differentiating between speech paradigms (perceived ...
11 hours ago · It is important to remember that FBCSP is a ML approach that extracts EEG features and then classifies them using a particular classifier such as LDA, SVM, etc ...
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16 hours ago · This study explores the potential of machine learning models trained on EEG-based features for depression detection. Six models and six feature selection ...
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6 hours ago · Signal Processing ML-Based Analysis: This includes the analysis of signals such as EEG (electroencephalogram), EMG (electromyogram), ECG (electrocardiogram), ...
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16 hours ago · Machine Learning Based Control of Integrated Drive and Charge Mode. Bi ... A Machine Learning Approach for Automated Classification of Skin. Fungal ...
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11 hours ago · Such multimodal technologies are well-suited to assess commu- nication disorders in particular, because they can capture changes in speech and voice (e.g., ...
13 hours ago · A ranked list of awesome machine learning Python libraries. Updated weekly. This curated list contains 920 awesome open-source projects with a total of 4.7M ...
15 hours ago · Additionally, we use machine learning and EEG to identify the neural underpinnings of different inner speech subtypes. Through these research avenues, LISN ...
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16 hours ago · autoMEA: Machine learning-based burst detection for multi-electrode array datasets ... Novel bias-reduced coherence measure for EEG-based speech tracking in ...
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