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Various researches had attempted to unveil the technique of virtuoso pianists using technologies. These researches employ different types of sensors in order to capture motion data of piano playing. Researches that embark on this area faced a common problem, the sensors used in these works are directly touching the pianist, in other words this causes a change of piano playing experience. Since piano...
This paper presents BrainChat, an augmented reality based multiuser concept for brain-computer interfaces. The goal is to provide seamless communication based only on thoughts. A working prototype is presented, which demonstrates two-person textual communication based on non-invasive brain computer interfaces. Design choices are discussed and directions for future work are provided, considering the...
Brain waves contain manifold information about ongoing cognitive processes and related body function. This information, such as power changes in certain frequency bands, can be extracted and interpreted by brain-computer interfaces (BCI). For this publication, we re-evaluated data from two subjects with implanted subdural electrodes who participated in a two-class BCI motor imagery experiment with...
In the realm of surface electromyography (sEMG) gesture recognition, deep learning algorithms are seldom employed. This is due in part to the large quantity of data required for them to train on. Consequently, it would be prohibitively time consuming for a single user to generate a sufficient amount of data for training such algorithms. In this paper, two datasets of 18 and 17 able-bodied participants...
This paper presents a study of a human-machine interface in the form of three parallel electromyographic bands placed around the user's forearm, with the influence of sEMG electrode layout on gesture recognition performance as the primary focus. Tested electrode configurations included setups ranging from 4 to 24 electrodes, with varying placement on the subject's forearm, using both monopolar and...
Epilepsy is defined as a collection of symptoms and clinical signs are emerging due to intermittent brain dysfunction, which occur due to loose or excessive abnormal electrical discharges of neurons in paroxysmal with various etiologies. In this article the implemented software detection of disease epilepsy, characteristics which will represent in the detection of epilepsy and not epilepsy are from...
A visual Brain-Computer Interface (BCI) speller is a system which assists disabled persons with severe neu-romuscular diseases to communicate with the external world. It acquires brain signals in response to visual stimuli shown to the person on a screen, and then analyzes in real-time to predict the desired symbol on a single trial basis. To date most BCI design paradigms have been focused on the...
Epileptic seizure source identification involves neurologists combing through a substantial amount of data manually, which sometimes takes weeks per patient. This paper presents a methodology for minimizing the amount of data a neurologist has to analyze to identify the seizure focus. The method keeps the neurologist as the final decision maker and aids in the decision making process. It has to be...
Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analyzed the repeatability of sEMG classification of hand grasps. The main goals of this paper are to explore repeatability in sEMG data and to release a repeatability...
Functional Electrical Stimulation (FES) cycling could benefit people with Spinal Cord Injury (SCI). The FES cycling involves large muscle groups during the training, and thus improves the cardiovascular function, increases the muscle bulk and reduces the secondary complications. This study developed an outdoor FES exercise cycling system for complete SCI persons to exercise their lower limbs without...
Electroactile feedback is crucial to close the loop systems of the teleoperation system, virtual reality system, and prosthetic system. Feedback devices are always limited in application due to large size for their inconvenience. In the present study, a wearable armband named “iFeel” is developed, which includes five pairs of electrodes for electrotactile stimulation is introduced for feedback to...
The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab, when performing hand grasp gestures. Participants (n = 26) took part in the study wearing the armband and producing a series of required gestures. Support vector machines were used to train a model using...
To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode...
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...
Cochlear implant surgery is performed to restore hearing in patients with a range of hearing disorders. To optimise hearing outcomes, trauma during the insertion of a cochlear implant electrode has to be minimised. Factors that contribute to the degree of trauma caused during surgery include: the location of the electrode, type of electrode, and the competence level of the surgeon. Surgical competence...
A supporting training system using a haptic device is considered to contribute more to motion learning than other stimuli that stimulate the human somatosensory system. However, the apparatus required for haptic stimulation is large, although the output is small. Electrical stimulation has been gaining attention in the field of human interfaces in recent years as a potential solution to this problem...
This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The training process is formulated as an optimisation problem with hardware in the loop. The liquid SWCNT/LC samples used here are un-configured and with nonlinear...
Wearable technology had set to play a major role in solving various problems in medical or sports applications in sports training, ergonomics, and mostly physical therapy. Surface electromyography (sEMG) has been used for localised muscle fatigue research area, although the success of sEMG based techniques is currently limited to isometric contraction and is not acceptable to the human movement community...
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...
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