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This research aims at investigating the relationship between Electroencephalogram (EEG) signals and human emotional states. A subject-independent emotion recognition system is proposed using EEG signals collected during emotional audio-visual inductions to classify different classes of continuous valence-arousal model. First, four feature extraction methods based on Approximate Entropy, Spectral entropy,...
Humanoids are to date still limited in reliable interpretation of social cues that humans convey which restricts fluency and naturalness in social human-robot interaction (HRI). We propose a method to read out two important aspects of social engagement directly from the brain of a human interaction partner: (1) the intention to initiate eye contact and (2) the distinction between the observer being...
In this research, we study the possibility of designing a mental-task based subject-independent Brain Computer Interface (BCI) using Electroencephalogram (EEG) signals. Due to major differences in the EEG signal of individuals during different mental tasks, designing a universal BCI seems impossible. Hence, almost all the previous studies concentrated on designing custom-based Brain Computer Interface...
Brain controlled wheelchair system is a Brain-computer interface (BCI) allowing people with severe neuromuscular disorder to control the navigation by themselves. Indeed, it replaces muscular activities by neurophysiological ones. The advantages of the Steady State Visual Evoked Potential (SSVEP) make it a favorable choice to be used in the BCI. The aim of the present study is to design a brain controlled...
Detection of Sleep onset is one of complex processes in the area of sleep medicine. The transition from wake state to sleep is termed as sleep onset and is identified using distinct markers like behavioural features, physiological features and changes in EEC Extraction of appropriate features from EEG recordings helps in automated recognition and classification of wake-sleep transition. This research...
This work focuses on non-linear characterization of 61-channel electroencephalogram (EEG) signal for detecting alcoholics using ranked Approximate Entropy (ApEn) parameters. Significant channels that contribute to the detection of alcoholism are selected by ranking the ApEn features based on ANOVA test. In order to classify alcoholics from control, the ranked feature set is applied to two non-linear...
This document describes the analysis of Electroenchaplogram (EEG) or brain signals using computational tool (LabVIEW) to interpret human thought such as moving forward, backward, turn right, turn left and to stop. This study is conducted to assist the disable people to communicate with external environment. The EEG signals are captured using wireless EEG amplifier while the subject in relax conditioin...
Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems. Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes. In this paper, we conduct a performance evaluation based on the noiseless...
This study emphases on the alpha oscillation of Electroencephalography (EEG) signal of normal children ability towards working memory performance and visual responsive. The assessments were conducted on 30 children aged between 7 to 9 years old who have no records of working memory disability. The raw EEG signals were decomposed using discrete wavelet transform with mother wavelet: Daubechies 4 (db4)...
The use of Electroencephalography (EEG) in the domain of Brain Computer Interface is a now common place. EEG for imagined speech reproduction and observation of brain response to audio stimuli are active areas of research. In this paper, we consider the case of imagined and mouthed non-audible speech recorded with EEG electrodes. We analyze different feature extraction techniques such as Mel Frequency...
Learning and memory are two related mental processes. EEG is a brain mapping technique, which can record brain states directly and can be used to assess learning and memory recall. In this paper, we will assess the effects of 2D and 3D educational contents on learning and memory recall by analyzing the brain states during recall tasks using EEG signals. 34 subjects learn same 2D and 3D educational...
Onset detection is one of the main issues towards self-paced BCIs that can be used outside research settings. For this reason, this paper suggests a potential solution for onset detection problem by discriminating between speech related events. In this study, overt, inhibited overt and covert states were tested to classify from idle state in an off-line setting. Autoregressive model coefficients were...
This paper presents an efficient VLSI implementation of a singular value decomposition (SVD) processor of on-line recursive independent component analysis (ORICA) for use in a real-time electroencephalography (EEG) system. ICA is a well-known method for blind source separation (BBS), which helps to obtain clear EEG signals without artifacts. In general, computations of ORICA are complicated and the...
Stability of algorithms is very important for electroencephalogram (EEG) based applications. Stable features should exhibit consistency among repeated measurements of the same subject. Previously, power features were reported to be one of the most stable EEG features in medical application. In this paper, stability of features in emotion recognition algorithms is studied. Our hypothesis is that the...
This paper presents the design and implementation of a real-time epilepsy detection filter that is suitable for closed-loop seizure suppression. The design aims to minimize the detection delay, while a reasonable average detection rate is obtained. The filter is based on a complex Morlet wavelet and uses an adaptive thresholding strategy for the seizure discrimination. This relatively simple configuration...
This paper explores how noise can improve classification accuracy of motor imagery classification using an ensemble support vector machine (ESVM) classifier. We add white Gaussian noise to the EEG signals and use them with the original signal data set for the ESVM training process. The ESVM classifier uses coefficients of the discrete wavelet transform (DWT) and coefficients of the autoregressive...
A wearable microsystem for low-latency automatic sleep stage classification and REM sleep detection in rodents is presented. The detection algorithm is implemented digitally to achieve low latency and is optimized for low complexity and power consumption. The algorithm uses both EEG and EMG signals as inputs. Experimental results using off-line signals from nine mice show REM detection sensitivity...
Near-infrared spectroscopy (NIRS) is a state-of-the-art non-invasive modality that measures changes in hemoglobin concentrations during tasks. More and more researches tried using NIRS as signal acquisition tool for brain-computer interface (BCI). In this paper, we conducted a study to test the feasibility of using single channel fNIRS in the building of a BCI system and whether it is possible to...
With shorter calibration times and higher information transfer rates, steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been studied most activity in recent years. Target identification is the ongoing core task in BCI researches, and plays a significant role in practical applications. In order to improve the performance of SSVEP-based BCI system, we proposed...
Effective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamic-object-directed (DOD) scenario respectively...
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