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The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted...
Objective: The purpose of this study was to determine the effect of Jenga game brain training for cognitive performance and EEG activity in Thai healthy older adults. Material and Methods: Six participants were participated. Participants were instructed to practice Jenga gam brain training. During practice memory and attention games, EEG activity were recorded by using the lightweight EEG device,...
Weight training is a one type of exercises which some people interest. When the body has a physical exercise which enough intensity, it can produce a positive effect on brain function by changing amplitude of EEG activity. This study examined the effect of an acute physical exercise by using bench press weight training on EEG activity in nine healthy young adults. The resent study demonstrated that...
The aim of this study was to investigate the effect of N-back task training to the memory system in obese patients indexed by the EEG power spectrum. Seven normal weight and seven obese patients were included in this study in order to evaluate the modifications of electroencephalographic (EEG) power spectra and EEG connectivity in obese patients. EEG activities were recorded in three minutes during...
This study aimed to evaluate the modifications of electroencephalographic (EEG) power spectra in overweight and obese patients. EEG was recorded while performing the Stroop Color Word Test. Stroop Color Word Test was performed and EEG activity was also monitored during the experiment. Paired t-test and independent t-test were used to show statistical difference between baseline and Stroop Color Word...
A patient-specific seizure detection system for Nocturnal Frontal Lobe Epilepsy (NFLE) is proposed. Data of several patients affected by NFLE, extracted from the EPILEPSIAE database, have been used for this study. As every patient possesses different physiological characteristics, several simulations were performed in order to find the best features to be extracted from electroencephalogram (EEG)...
This study aimed to confirm the possibility of using flickering video for mirror neuron action and SSVEP evocation, which is useful for the BCI rehabilitation game. Subjects were asked to watch the videos of upper limb motion and visual white noise. The videos were flickered at a rate of 20 Hz. Twenty subjects were recruited and asked to watch the flickering videos while an EEG signal was recorded...
The brain–computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient...
In this paper, the single-channel EEG based classification systems using simple extracted features are investigated. Each classification system contains the following stages: data acquisition, signal decomposition, feature extraction, and classification. In addition to using the filter bank and empirical mode decomposition (EMD) methods for signal decomposition, a sparse discrete wavelet packet transform...
The human life becomes increasingly stressful and not everyone can manage his/her own life well. Most people are not aware of stress even though stress is a common illness that impacts on daily life, including family, relationships, and studying. Moreover, stress affects health, both physically and mentally at all ages. When people suffer from stress repeatedly, stress will turn to be multiple physical...
Restoring normal walking abilities following the loss of them is a challenge. Importantly, there is a growing need for a better understanding of brain plasticity and the neural involvements for the initiation and control of these abilities so as to develop better rehabilitation programmes and external support devices. In this paper, we attempt to identify gait-related neural activities by decoding...
In Brain Computer Interfaces (BCIs), with multiple recordings from different subjects in hand, a question arises regarding whether the knowledge of previously recorded subjects can be transferred to a new subject. In this study, we explore the possibility of transferring knowledge by using a convolutional network model trained on multiple subjects and fine-tuning the model on a small amount of data...
P300-based brain-computer interface (BCI) is one of the most common BCIs. Due to the characteristics of P300 responses vary from person to person, it leads to the necessity of collecting much labeled data from each user and the problem of time-consuming in many applications. In this work, a transfer learning method which dynamically adjusts the weights of instances is applied to improve the P300-based...
In P300 speller brain-computer interface (BCI), the stimulus sequence is presented to subject for several rounds to achieve reliable P300 detection. Traditionally, the number of rounds is fixed and relatively large (e.g., 15 in the Wadsworth Dataset of BCI Competition 2005), which results in low information transfer rate. In order to improve the speed of character recognition without affecting the...
In this paper, a novel electroencephalographic (EEG) based mind controlled virtual-human obstacle-avoidance platform (EEG-MC-VHOAP) is designed to improve brain computer interface (BCI) systems and offer a new game. With the EEG-MC-VHOAP, subjects can use their brain signals to control a virtual human to have a training of avoiding obstacles in a three dimensional (3D) environment. The EEG-MC-VHOAP...
Electroencephalography (EEG) and brain-computer interfaces (BCI) are receiving increasing attention and expanding application in stroke study. To identify stroke patients and normal controls during mental rotation task, common spatial pattern (CSP) algorithm is employed to extract features from binary-class EEG which will be further to form the dictionary for sparse representation. In the classification...
Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number...
Despite the recent increasing interest in biometric identification using electroencephalogram (EEG) signals, the state of the art still lacks a simple and robust model that is useful in real applications. This work proposes a new approach based on convolutional neural network CNN. The proposed CNN works directly on raw EEG data, thus alleviating the need for engineering features. We investigate the...
As a new biometric, the Electroencephalogram (EEG) signal has the advantages of invisibility, non-clonability, and non-coercion compare to traditional biometrics. However, the real-time and stability are the difficulties that the current EEG-based person authentication systems face. In this paper, we design a real-time and stable person authentication system using EEG signals, which are elicited by...
Multi-target stimulus coding plays an important role in a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). In conventional SSVEP-based BCIs, a large interval between two neighboring stimulus frequencies is often used to improve classification accuracy. Although recent progresses in stimulus coding and target identification methods that have significantly improved...
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