This paper presents a novel electro-encephalography (EEG) signal processing chain designed to classify two levels of mental fatigue that appears after having spent a long time on a tedious task. The decrease in vigilance associated with mental fatigue makes it a dangerous state for operators in charge of complex systems. The processing chain, inspired from active brain computer interface comput...

The common spatial patterns (CSP) algorithm is the most popular filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. effectiveness of CSP depends on optimal selection frequency band and time window from EEG. Many algorithms have been designed optimize CSP, while few seek window. This study proposes a novel f...

Motor imagery is a common control strategy in EEG-based brain-computer interfaces (BCIs). However, voluntary control of sensorimotor (SMR) rhythms by imagining a movement can be skilful and unintuitive and usually requires a varying amount of user training. To boost the training process, a whole class of BCI systems have been proposed, providing feedback as early as possible while continuously ...

Spatial filtering (SF) constitutes an integral part of building EEG-based brain-computer interfaces (BCIs). Algorithms frequently used for SF, such as common spatial patterns (CSPs) and independent component analysis, require labeled training data for identifying filters that provide information on a subject's intention, which renders these algorithms susceptible to overfitting on artifactual E...

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor calle...

One source of EEG data quality deterioration is noise. The others are artifacts, such as the eye blinking, oculogyration, heart beat, or muscle activity. All these factors mentioned above contribute to the disappointing and poor quality of EEG signals. There are some solutions which allow increase of this signals quality. One of them is Common Spatial Patterns. Some scientific papers report tha...

The number of people with disabilities is increasing, so it requires bionic devices to replace human motor functions. Brain-Computer Interface (BCI) can be a tool for the device communicate brain. Signal brain or Electroencephalogram (EEG) signal need classify drive corresponding device. This research goal imagination right and left-hand movements based on EEG signal. system design in this cons...

This paper is a compilation of the most recent machine learning methods used in the Berlin Brain-Computer Interface. In the field of Brain-Computer Interfacing, machine learning has been mainly used to extract meaningful features from noisy signals of large dimensionality and to classify them to transform them into computer commands. Recently, our group developed different methods to deal with ...

During the last years interest has been growing to find an effective communication channel which translates human intentions into control signals for a computer, the so called Brain-Computer Interface (BCI). One main goal of research is to help patients with severe neuromuscular disabilities by substituting normal motor outputs. Various cortical processes were identified which are suitable for ...