Related papers: Classification of EEG recordings in auditory brain…

This paper discusses the use of the scalp electric field to decode EEG-recorded brain processes. Instead of using bipolar measurements, the scalp electric field is described as a 3-vector field whose orthogonal components are obtained from…

Estimation of brain functional connectivity from EEG data is of great importance both for medical research and diagnosis. It involves quantifying the conditional dependencies among the activity of different brain areas from the time-varying…

Electroencephalography (EEG) reflects the brain's functional state, making it a crucial tool for diverse detection applications like seizure detection and sleep stage classification. While deep learning-based approaches have recently shown…

The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques. However, even deep learning…

Carrying conversations in multi-sound environments is one of the more challenging tasks, since the sounds overlap across time and frequency making it difficult to understand a single sound source. One proposed approach to help isolate an…

Advances in neuroscience and artificial intelligence have enabled preliminary decoding of brain activity. However, despite the progress, the interpretability of neural representations remains limited. A significant challenge arises from the…

Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement…

Accurate classification of sleep stages is crucial for the diagnosis and management of sleep disorders. Conventional approaches for sleep scoring rely on manual annotation or features extracted from EEG signals in the time or frequency…

The decoding of linguistic information from electroencephalography (EEG) signals remains an extremely challenging problem in brain-computer interface (BCI) research. In particular, sentence-level decoding from EEG is difficult due to the…

This article develops a general detection theory for speech analysis based on time-varying autoregressive models, which themselves generalize the classical linear predictive speech analysis framework. This theory leads to a computationally…

In the medical field, current ECG signal analysis approaches rely on supervised deep neural networks trained for specific tasks that require substantial amounts of labeled data. However, our paper introduces ECGBERT, a self-supervised…

The use of EEG signal to diagnose several brain abnormalities is well-established in the literature. Particularly, epileptic seizure can be detected using EEG signals and several works were done in this field. The joint time-frequency…

There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is…

Epilepsy is one of the most common and yet diverse set of chronic neurological disorders. This excessive or synchronous neuronal activity is termed seizure. Electroencephalogram signal processing plays a significant role in detection and…

Voice signal classification based on human behaviours involves analyzing various aspects of speech patterns and delivery styles. In this study, a real-time dataset collection is performed where participants are instructed to speak twelve…

With the recent success of artificial intelligence in neuroscience, a number of deep learning (DL) models were proposed for classification, anomaly detection, and pattern recognition tasks in electroencephalography (EEG). EEG is a…

Self-supervised learning models for speech processing, such as wav2vec2, HuBERT, WavLM, and Whisper, generate embeddings that capture both linguistic and paralinguistic information, making it challenging to analyze tone independently of…

Epilepsy is one of the common neurological disorders characterized by recurrent and uncontrollable seizures, which seriously affect the life of patients. In many cases, electroencephalograms signal can provide important physiological…

Electroencephalography (EEG) signals' interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new…

EEG preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We…