AIMC Topic: Electroencephalography

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Electroencephalography Decoding with Conditional Identification Generator.

International journal of neural systems
Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding human-artificial intelligence (AI) interaction systems. Recent advancements in deep neural networks (DNNs) have demonstrated significant promise in thi...

Recurrent and convolutional neural networks in classification of EEG signal for guided imagery and mental workload detection.

Scientific reports
The Guided Imagery technique is reported to be used by therapists all over the world in order to increase the comfort of patients suffering from a variety of disorders from mental to oncology ones and proved to be successful in numerous of ways. Poss...

Select for better learning: identifying high-quality training data for a multimodal cyclic transformer.

Journal of neural engineering
. Tonic-clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epilepsy, require accurate detection to enable effective long-term monitoring. Previous studies have demonstrated the advantages of multimodal seizure det...

Eye movement detection using electrooculography and machine learning in cardiac arrest patients.

Resuscitation
AIM: To train a machine learning algorithm to identify eye movement from electrooculography (EOG) in cardiac arrest (CA) patients. Neuroprognostication of comatose post-CA patients is challenging, requiring novel biomarkers to guide decision making. ...

Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI.

Sensors (Basel, Switzerland)
In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language models within the field of human-robot interaction and human-computer interaction. While research in...

Using machine learning to simultaneously quantify multiple cognitive components of episodic memory.

Nature communications
Why do we remember some events but forget others? Previous studies attempting to decode successful vs. unsuccessful brain states to investigate this question have met with limited success, potentially due, in part, to assessing episodic memory as a u...

Enhancing Schizophrenia Diagnosis Through Multi-View EEG Analysis: Integrating Raw Signals and Spectrograms in a Deep Learning Framework.

Clinical EEG and neuroscience
Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals' lives. Early detection is crucial for improving treatment outcomes, but the diagnostic p...

EEG detection and recognition model for epilepsy based on dual attention mechanism.

Scientific reports
In the field of clinical neurology, automated detection of epileptic seizures based on electroencephalogram (EEG) signals has the potential to significantly accelerate the diagnosis of epilepsy. This rapid and accurate diagnosis enables doctors to pr...

Detection of freely moving thoughts using SVM and EEG signals.

Journal of neural engineering
Freely moving thought is a type of thinking that shifts from one topic to another without any overarching direction or aim. The ability to detect when freely moving thought occurs may help us promote its beneficial outcomes, such as for creative thin...

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain.

Scientific reports
Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients w...