Submitted to IEEE Transactions on Neural Systems and Rehabilitation Engineering. In this work, we have investigated the effect of EEG data preprocessing on the performance of deep learning models. In ...

More surprisingly, it was shown that model performance was close to being the same whether the input was EEG data or simply pure noise, which gives reason to suspect models often depend on memorized ...

Machine Learning (ML) and Deep Learning (DL) algorithms can reduce road accidents through their ability to detect sleepy ...

Abstract: The primary aim of this study was to assess the classification performance of deep learning models in distinguishing between resting state and motor imagery swallowing, utilizing various ...

In this study, we aim to collect EEG signals from 13 subjects performing four MI tasks of the unilateral upper limb: arm lifting/lowering and forearm pronation/supination. Moreover, we propose a ...

A new study from Sharjah University has unveiled an innovative machine learning (ML) and deep learning (DL) algorithm ...

In this study, we introduced a novel deep learning approach, called SleepEEGNet, for automated sleep stage scoring using a single-channel EEG. We evaluated our model using the Physionet Sleep-EDF ...

Kaunas University of Technology developed a multimodal AI system integrating EEG and speech data to diagnose depression with ...

To achieve this, I will use the Maixduino Kit for AI and IoT. The Maixduino board processed the EEG signals using a machine ...

Here are five brain facts that are now brain fiction.

AI will need to learn how to justify the diagnosis The collected EEG and audio signals were transformed into spectrograms, ...