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Fast and accurate discrimination of Electroencephalography (EEG) data is necessary for controlling brain machine interface. This paper introduces a novel method to discriminate 2-class motor imagery states (left and right hand) using nonnegative matrix factorization (NMF), common spatial pattern (CSP) and random forest. Conventionally CSP is used after extracting frequency band segment of EEG signal,...
This research investigated the potential of a new method for onset detection towards asynchronous BCIs. Siren sound covert production and recall were classified against the idle (no task) state in an off-line system. Wavelet packet decomposition was employed for feature extraction and a Support Vector Machine (SVM) was used for classification. Three window segments lengths were tested (1s, 2s and...
Decoding mental processes in single trials is one of the prerequisites for tailoring learning paradigms, which aim at improving performance in cognitive tasks. In this study user choices are predicted in a matrix reasoning task. By employing multivariate analysis techniques we are able to show that it is possible to decode the subjects' answers prior to their response by means of ERP-based EEG data...
This paper aims to investigate the effects of steady-state visual evoked potential (SSVEP) in the aspects of viewing distance variation for a smart TV control system. We designed an experimental environments with different viewing distance. Four healthy people (age 27.5±1, male) participated in the experiment. Four visual stimuli with a round shape were designed and presented on LED monitor flickering...
Sleep quantity affects an individual's personal health. The gold standard of measuring sleep and diagnosing sleep disorders is Polysomnography (PSG). Although PSG is accurate, it is expensive and it lacks portability. A number of wearable devices with embedded sensors have emerged in the recent past as an alternative to PSG for regular sleep monitoring directly by the user. These devices are intrusive...
Emotion play an important role at several activities in the present world. Human decision making, cognitive process and interaction between human & machine all the activities depends on human emotions. Facial expression, musical activities and several approaches used to find the human emotions. In this paper EEG is used to find the accurate emotion. Emotion classification is the huge task. Classification...
P300-speller is a communication style based on Brain-computer interface (BCI) which allows users to input characters by electroencephalography (EEG) signals. In the past few years, there are various studies on P300-speller paradigm and classification algorithm. However, the accuracy and bit rates are not yet satisfied for our daily life. In order to improve the performance of the P300-speller, we...
Recently, systems using motor imagery (MI) have been developed as practical examples of brain-computer interface (BCI). Electroencephalography (EEG) was used to generate an electroencephalogram of elbow flexion. In addition, a method was proposed to extract the feature values that would enable the recognition right- or left-handed elbow flexion MI. In the proposed method, fast Fourier transform overlap...
In this paper, a method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) signals recorded before the impact between the putter and the ball. This method can be used into a brain-computer interface system that encourages golfers for putting when their EEG patterns show that they are ready. In the proposed method, multi-channel EEG trials of a golfer are...
BRAIN computer interface (BCI) is a communication technique that aims to detect and identify brain intents and translate them into machine commands to control the operation of electrical and/or mechanical devices. Electroencephalography (EEG) is a widely used imaging technique for noninvasive BCI. Due to EEG non-stationarity, which is typically caused by variation of head size, electrode positions...
As human brain activities, represented by EEG brainwave signals, are more confidential, sensitive, and hard to steal and replicate, they hold great promise to provide a far more secure biometric approach for user identification and authentication. In this study, we present an EEG-based biometric security framework. Specifically, we propose to reduce the noise level through ensemble averaging and low-pass...
An auditory loss is one of the most common disabilities present in newborns and infants in the world. A conventional hearing screening test's applicability is limited as it requires a feedback response from the subject under test. To overcome such problems, the primary focus of this study is to develop an intelligent hearing ability level assessment system using auditory evoked potential signals (AEP)...
An electroencephalographic (EEG) waveform could be denoted by a series of ordinal patterns called motifs which are based on the ranking values of subsequence time series. Permutation entropy (PE) has been developed to describe the relative occurrence of each of these motifs. However, PE has few limitations, mainly its inability to differentiate between distinct patterns of a certain motif, and its...
This paper aims to improve tactile and bone-conduction brain computer interface (tbaBCI) classification accuracy based on a new stimulus pattern search in order to trigger more separable P300 responses. We propose and investigate three approaches to stimulus spatial and frequency content modification. As result of the online tbaBCI classification accuracy tests with six subjects we conclude that frequency...
A brain-computer interface (BCI) based on steady-state visual evoked potentials (SSVEP) is one of the most practical BCI, because of high recognition accuracies and short time training. Phase of SSVEPs can be potentially applicable for generating device commands. However, the effective method of estimating the phase of SSVEPs has not yet been established, especially, in the case of using multi-channel...
A novel approach to steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) is presented in the paper. To minimize possible side-effects of the monochromatic light SSVEP-based BCI we propose to utilize chromatic green-blue flicker stimuli in higher, comparing to the traditionally used, frequencies. The developed safer SSVEP responses are processed an classified with features...
This paper reports on a project aiming to confirm whether a tactile stimulator "touch-sense glove" is effective for a novel brain-computer interface (BCI) paradigm and whether the tactile stimulus delivered to the fingers could be utilized to evoke event related potential (ERP) responses with possible attentional modulation. The tactile ERPs are expected to improve the BCI accuracy. The...
Brain computer interface (BCI) enables disabled people to communicate by brain signal. P300 which appears 300ms after the onset of a low frequent stimulus is extensively used to actualize BCI. Precise detection of P300 component is therefore important. Most of existing BCI assumes that P300 is observed after 300ms, however this latency has variation due to the condition of a subject and the level...
Steady-state visual evoked potentials (SSVEPs) enable brain-computer interfaces to achieve efficient performance in command detection accuracy and information transfer rate (ITR). However, a limited bandwidth of SSVEPs causes a limited number of possible command in BCIs. Moreover since the amplitude of SSVEP at a particular frequency depends on users, some BCI commands could be executed easily (higher...
This paper is aimed to predict pain perception from laser-evoked EEG oscillatory activities in the time-frequency domain with multivariate pattern analysis (MVPA). We first identify pre-/post-stimulus EEG oscillatory activities that are correlated with the intensity of laser-evoked pain perception using a multivariate linear regression (MVLR) model, which is solved by partial least-squares regression...
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