In this paper, an Artificial Neural Network (ANN) algorithm for classifying the EEG spectrogram images in brainwave is presented. Gray Level Co-occurrence Matrix (GLCM) texture feature from the EEG spectrogram images have been used as input to the system. The GLCM texture feature produced large dimension of feature, therefore the Principal Component Analysis (PCA) is used to reduce the feature ...

The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework whi...

Pathological slowing in the electroencephalogram (EEG) is widely investigated for diagnosis of neurological disorders. Currently, gold standard detection visual inspection EEG by experts, which time-consuming and subjective. To address those issues, we propose three automated approaches to detect EEG: Threshold-based Detection System (TDS), Shallow Learning-based (SLDS), Deep (DLDS). These syst...

In this paper, we present a novel methodology to solve the classification problem, based on sparse (data-driven) regressions, combined with techniques for ensuring stability, especially useful for high-dimensional datasets and small samples number. The sensitivity and specificity of the classifiers are assessed by a stable ROC procedure, which uses a non-parametric algorithm for estimating the ...

We present a transfer learning-based approach for decoding imagined speech from electroencephalogram (EEG). Features are extracted simultaneously multiple EEG channels, rather than separately individual channels. This helps in capturing the interrelationships between cortical regions. To alleviate problem of lack enough data training deep networks, sliding window-based augmentation is performed...

In recent years, Brain-Computer Interface (BCI) system gained much popularity since it aims at establishing the communication between human brain and computer. BCI systems are applied in several research areas such as neuro-rehabilitation, robots, exoeskeletons, etc. Electroencephalography (EEG) is a technique commonly capturing signals. It incorporated has attractive features non-invasive natu...

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational content...

Prior experience with a stimulus leads to multiple forms of learning that facilitate subsequent behavior (repetition priming) and neural processing (repetition suppression). Learning can occur at the level of stimulus-specific features (stimulus learning), associations between stimuli and selected decisions (stimulus-decision learning), and associations between stimuli and selected responses (s...

Classification of electroencephalograph (EEG) data is the common denominator in various recognition tasks related to EEG signals. Automated recognition systems are especially useful in cases when continuous, long-term EEG is recorded and the resulting data, due to its huge amount, cannot be analyzed by human experts in depth. EEG-related recognition tasks may support medical diagnosis and they ...