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EEG signals are neuroelectric signals which helps in analyzing the brain state of the Human being. Extracting Features of EEG signal in real time is the most challenging task because of its high variability and complexity. In this paper we have used FPGA (Virtex-5) for extracting the features in real time for Brain Computer Interface applications. Nowadays, Brain Computer Interface (BCI) plays a major...
This paper examines the quality of feature set obtained from Wavelet based Energy-entropy with variation of scale and wavelet type. Here motor imagery of left-right hand movement classification problem has been studied. Elliptic bandpass filters are used to discard unwanted signals and also to extract alpha & beta rhythms. We have implemented wavelet-based energy-entropy with three level of decomposition...
Brain-computer Interfaces (BCIs) are control and communication systems based on acquisition and processing of brain signals to control a computer or an external device. Usually, BCI is focused in recognizing acquired events by different neuroimage methods, but the most used is the electroencephalography (EEG). Feature extraction over EEG signals for BCI systems is crucial to the classification performance...
Brain signals arise as a mixture of various neural processes that occur in different spatial, frequency and temporal locations. In detection paradigms, algorithms are developed that target specific processes. In this work, we apply tensor factorisation to a set of intracranial electroencephalography data from a group of epileptic patients and factorise the data into three modes; space, time and frequency...
RSVP Keyboard™ is a non-invasive electroencephalography (EEG) based brain computer interface (BCI) for letter by letter typing. In this system a sequence of symbols is presented on a computer screen in rapid serial visual presentation scheme to query a user's intent. EEG evidence and language model are used in conjunction for joint inference of the intended symbol. Usually repetition of sequences...
Electroencephalogram (EEG) is a gold standard in epilepsy diagnosis and has been widely studied for epilepsy-related signal classification, such as seizure detection or focus localization. In the past few years, discrete wavelet transform (DWT) has been widely used to analyze epileptic EEG. However, one practical question unanswered is the optimal levels of wavelet decomposition. Deeper DWT can yield...
We investigate the neural correlates of visual working memory using electroencephalography (EEG). Our objective is to develop a cognitive Brain-Computer Interface (BCI) able to monitor visual working memory load in real-time. A system with these properties would eventually have different applications, such as training, rehabilitation, or safety while operating dangerous machinery. The BCI performances...
Depth of anesthesia is a matter of great importance in surgery. Determination of depth of anesthesia is a time consuming and difficult task carried out by experts. This study aims to decide a method that can classify EEG data automatically with a high accuracy and, so will help the experts for determination process. This study consists of three stages: feature extraction of EEG signals, feature selection,...
Brain-Computer Interfaces (BCIs) often rely on low-level cognitive processes known to be impaired in late stages of amyotrophic lateral sclerosis (ALS). We propose a BCI for ALS patients based on self-regulation of neuronal oscillations in the superior parietal lobule, which is less affected by ALS than motor and sensory cortices. We describe a case of self-regulation of band power in gamma range...
Emblematic gesture pictures were presented to subjects as probes in relation to semantically congruent and incongruent sentences to investigate if there is a similar cognitive processing network for congruity as there is with words. Subjects had to perform a simple discrimination task while undergoing EEG recordings. The ERPs elicited by semantically incongruent gestures produced larger N400 and possibly...
Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. This paper investigates the performance of novel feature extraction based on a signal...
In a brain-computer interface, using event-related desynchronization and synchronization(ERD and ERS), intuitive command input is available. Switching precision was thought to be improved by a detailed examination of the ERD ���� rhythm in the time-frequency domain. In the experiment, measured electroencephalograms of motor cortex in the resting state, hand grasp motion, and hand grasp recall were...
A brain-computer interface (BCI) character input experiment focusing on participants’ BCI intelligibility was performed. In theory, a BCI can be operated by anyone if cognitive activity is possible. However, individual differences clearly occur in practice. Therefore, we supposed that this difference was related to BCI intelligibility. In a previous study, BCI experts and BCI novice users were compared...
In this paper, we develop a model of braincontrolled vehicles. This model includes an extended driver model based on the Queuing-network cognitive architecture, a brain-computer interface (BCI) model representing the performance of the BCI system that can issue three classes of direction control commands, an interface model converting the actual steering command from the BCI system to the steering...
Electroencephalographic (EEG) patterns are electrical signals generated in association with neural activities. Most anomalies in brain functioning manifest with their signature characteristics in EEG pattern. Epileptic seizure, which is a brain abnormality well-studied through EEG analysis, is an abnormal harmonious neural activity in the brain characterized by the presence of spikes in EEG. An automated...
Occurrence of multiple seizures is a common phenomenon observed in patients with epilepsy: a neurological malfunction that affects approximately 50 million people in the world. Seizure prediction is widely acknowledged as an important problem in the neurological domain, as it holds promise to improve the quality of life for patients with epilepsy. A noticeable number of clinical studies showed evidence...
Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multichannel based. Multichannel based sleep scoring is not...
This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90º left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent...
EEG based biometric system can be used for authentication, with advantages like confidentiality retention and forgery prevention. Signals which are taken from maximum brain regions show some sort of unique information that can be used for extracting the subject dependent pattern. This paper presents an approach to find the relationships among signals generated in different brain regions which give...
Motion Onset Visually Evoked Potentials (mVEPs) are elicited by visual stimuli that offer a more elegant, less fatiguing visual presentation than other stimuli used in visual evoked potentials (VEPs) studies. mVEP for use in brain computer interface (BCI) video gaming offer users a pleasant presentation environment to play video games. Modern, commercially available video games are a popular form...
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