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High dimensionality of feature space is a problem in supervised machine learning. Redundant or superfluous features either slow down the training process or dilute the quality of classification. Many methods are available in literature for dimensionality reduction. Earlier studies explored a discernibility matrix (DM) based reduct calculation for dimensionality reduction. Discernibility matrix works...
Effective rehabilitation of children with cerebral palsy (CP) requires intensive task-specific exercise but many in this population lack the motor capabilities to complete the desired training tasks. Providing robotic assistance is a potential solution yet the effects of this assistance are unclear. We combined a novel exoskeleton and exercise video game (exergame) to create a new rehabilitation paradigm...
Electroencefalography (EEG) has a wide range of applications in human-computer interaction and in adaptation and personalization of the interfaces. It can be used either as a sensor, e.g., for emotion detection, or as an input device that allows to take actions based on the brain's response to the presented stimuli. For the latter, it is crucial to be able to reliably detect event-related potentials...
Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used...
Motor Imagery (MI), the mental execution of an action, is widely applied as a control modality for electroencephalography (EEG) based Brain-Computer Interfaces (BCIs). Different approaches to MI have been implemented, namely visual observation (VMI) or kinesthetic rehearsal (KMI) of movements. Although differences in brain activity during VMI or KMI have been studied, no investigation with regards...
The objectives were to investigate and understand the effects of Inner Wisdom Training and Meditation to EEG power spectra. The samples included six healthy adults with age ranged between twenty to sixty years. All participants were equally attended all nine levels of Inner Wisdom Meditation Program. During the EEG activity recording, all participants were asked to close their eyes for three minutes...
The purpose of this study was to determine the effect of brain training for cognitive performance and EEG activity in the digital media officers. Seven participants working as digital media position, were participated in this study. Participants were instructed to practice Sustained Attention to Response Task (SART) which include many skills including speed; memory, attention, flexibility and problem...
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...
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...
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