Information about the emotional state of users has become more and more important in human-machine interaction and braincomputer interface. This paper introduces an emotion recognition system based on electroencephalogram (EEG) signals. Experiments using movie elicitation are designed for acquiring subject’s EEG signals to classify four emotion states, joy, relax, sad, and fear. After pre-proce...

The article considers modern technologies for reading signals from the human brain and nervous system selects optimal technology to improve efficiency of adult learning with help a neurocomputer interface. Existing brain-computer interfaces (BCI) can be divided into invasive non-invasive. first, BCIs, are neuroimplants in certain parts that work on basis electrocorticography (ECOG) or intracran...

Almost every device we interact with relies the use of a physical control interface in order for the user to command the system. This “one-size fits all approach” to control interfacing prevents the almost 3 million severely paralyzed persons from being able to operate even devices specifically created to help physically disabled persons. Robotic aids have been developed to move physical assets...

The Electroencephalograph (EEG) signals is one of the most widely used in the bioinformatics field due to its rich information about human tasks. The Electroencephalogram is a neuronal activity that represents the electrical activity of the brain. The uses of EEG signals in the field of Brain computer Interface (BCI) have obtained a lot of interest with diverse applications ranging from medicin...

The target of this research is decrease or eliminate the training stage, when a new subject uses a braincomputer interface (BCI) based on imagined speech. In the training phase it is necessary to acquire enough information to identify the patterns that allow to distinguish what the subject imagines to pronounce. Then, in imagined speech, like any other evoked potential, the training process is ...

The P300 Speller has proven to be an effective paradigm for braincomputer interface (BCI) communication. Using this paradigm, studies have shown that a simple linear classifier can perform as well as more complex nonlinear classifiers. Several studies have examined methods such as Fisher’s Linear Discriminant (FLD), Stepwise Linear Discriminant Analysis (SWLDA), and Support Vector Machines (SVM...

A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based braincomputer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from curr...

Many individuals with severe and multiple disabilities do not have an access pathway that enables them to interface with their environment because they are not afforded a binary switch that they can reliably control. While recent research has focused on the self-regulation of central signals of the autonomic nervous system (ANS) to create braincomputer interfaces (BCIs) for these individuals, t...

Abstract. Previously we contributed to the development of a braincomputer interface (Brainput) using functional near infrared spectroscopy (NIRS). This NIRS-based BCI was designed to improve performance on a human-robot team task by dynamically adapting a robot’s autonomy based on the person’s multitasking state. Two multitasking states (corresponding to low and high workload) were monitored in...

Individuals suffering from locked-in syndrome are completely paralyzed and unable to speak but otherwise cognitively intact. Traditional assistive technology is ineffective for this population of users due to the physical nature of input devices. Brain-computer and biometric interfaces offer users with severe motor disabilities a non-muscular input channel for communication and control, but req...