This paper discusses the various methods of identifying the seizure onset zone (SOZ) from
the intracranial electroencephalography (iEEG) data. Epilepsy, also known as seizure …

The present study applies the one-dimensional convolutional neural network (1D-CNN) to
propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The …

Background A reliable biomarker to identify cortical tissue responsible for generating
epileptic seizures is required to guide prognosis and treatment in epilepsy. Combined spike …

Brain-computer interfaces (BCIs) are currently integrated into traditional rehabilitation
interventions after stroke. Although BCIs bring many benefits to the rehabilitation process …

Many deep-learning-based seizure detection algorithms have achieved good classification,
which usually outperformed traditional machine-learning-based algorithms. However, the …

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy
detection. Recently sparse representation-based classification (SRC) methods have …

Presurgical investigations for categorizing focal patterns are crucial, leading to localization
and surgical removal of the epileptic focus. This paper presents a machine learning …

Objective. High-frequency oscillations (HFOs) are considered a biomarker of the
epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors …

The need for automatic detection and classification of high-frequency oscillations (HFOs) as
biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the …

Quadcopter is an important way for the human to explore the physical world. The brain-
computer interface (BCI) technology is used to control the quadcopter flight in order to help …