BackgroundBrain activation differs according to lesion location in functional magnetic resonance imaging (fMRI) studies, but lesion location-dependent electroencephalographic (EEG) alterations are unclear. Because of the increasing use of EEG-based brain-computer-interface rehabilitation, we examined lesion location-dependent EEG patterns in patients with stroke while they performed motor tasks.MethodsTwelve patients with chronic stroke were divided into three subgroups according to their lesion locations: supratentorial lesions that included M1 (SM1+), supratentorial lesions that excluded M1 (SM1-), and infratentorial (INF) lesions. Participants performed three motor tasks [active, passive, and motor imagery (MI)] with supination and grasping movements. The hemispheric asymmetric indexes, which were calculated with laterality coefficients (LCs), the temporal changes in the event-related desynchronization (ERD) patterns in the bilateral motor cortex, and the topographical distributions in the 28-channel EEG patterns around the supplementary motor area and bilateral motor cortex of the three participant subgroups were compared with those of the 12 age-matched healthy controls.ResultsThe SM1+ group exhibited negative LC values in the active and MI motor tasks, while the other patient subgroups exhibited positive LC values. Negative LC values indicate that the ERD/ERS intensity of the ipsilateral hemisphere is higher than the contralateral hemisphere, whereas positive LC values indicate that the ERD/ERS intensity of the contralateral hemisphere is higher than the ipsilateral hemisphere. The LC values of SM1+ and healthy controls differed significantly (rank-sum test, p < 0.05) in both the supination and grasping movements in the active task. The three patient subgroups differed distinctly from each other in the topography analysis.ConclusionsThe hemispheric asymmetry and topographic characteristics of the beta band power patterns in the patients with stroke differed according to the location of the lesion, which suggested that EEG analyses of neurorehabilitation should be implemented according to lesion location.Electronic supplementary materialThe online version of this article (doi:10.1186/s12984-016-0120-2) contains supplementary material, which is available to authorized users.

The increase in the number of adolescents with internet gaming disorder (IGD), a type of behavioral addiction is becoming an issue of public concern. Teaching adolescents to suppress their craving for gaming in daily life situations is one of the core strategies for treating IGD. Recent studies have demonstrated that computer-aided treatment methods, such as neurofeedback therapy, are effective in relieving the symptoms of a variety of addictions. When a computer-aided treatment strategy is applied to the treatment of IGD, detecting whether an individual is currently experiencing a craving for gaming is important. We aroused a craving for gaming in 57 adolescents with mild to severe IGD using numerous short video clips showing gameplay videos of three addictive games. At the same time, a variety of biosignals were recorded including photoplethysmogram, galvanic skin response, and electrooculogram measurements. After observing the changes in these biosignals during the craving state, we classified each individual participant’s craving/non-craving states using a support vector machine. When video clips edited to arouse a craving for gaming were played, significant decreases in the standard deviation of the heart rate, the number of eye blinks, and saccadic eye movements were observed, along with a significant increase in the mean respiratory rate. Based on these results, we were able to classify whether an individual participant felt a craving for gaming with an average accuracy of 87.04%. This is the first study that has attempted to detect a craving for gaming in an individual with IGD using multimodal biosignal measurements. Moreover, this is the first that showed that an electrooculogram could provide useful biosignal markers for detecting a craving for gaming.

Neurohaptics is the field of study that strives to understand the complex neural representation provoked in response to tactile and/or kinesthetic stimuli. This field has garnered a noticeable attention over the past decade not only in neuro-scientific research but also in medical, marketing and engineering fields. In this paper, we review existing literature on Electroencephalography (EEG)-based neurohaptic studies charting out the main themes and significant findings. Furthermore, we provide a brief review of the EEG analytical methods commonly utilized in the neurohaptic domain. Also, we present a case study with the complete flow of conducting neurohaptic research studies. Lastly, we discuss limitations and provide directions for future neurohaptic research, such as: modeling quality of haptic experience, improving neurohaptic systems and neurohatpics in virtual reality.

It has been shown in previous studies that haptic guidance improves the learning outcomes of handwriting motor skills. Full and partial haptic guidance are developed and evaluated in the literature. In this paper, we present two experimental studies to examine whether combining full and partial haptic guidance is more effective for improving handwriting skills than merely full or partial guidance methods. Experiment I, with 22 participants, compares the effectiveness of merely full and partial haptic guidance methods towards improving learning outcomes of Arabic handwriting. Even though haptic guidance in general is found to be effective and pleasant by all participants, experiment I concludes that there are no statistically significant differences in the learning outcomes between full and partial haptic guidance. Experiment II investigates whether a combination of full and partial haptic guidance could further improve the learning outcomes, compared to merely full or partial haptic guidance. The learning outcomes and quality of experience are measured to evaluate each group's performance. Results from experiment II demonstrate that the combination of full and partial haptic guidance results in statistically significant improvements in the quality of handwriting, compared to mere full or partial haptic guidance. In particular, starting with partial haptic guidance at early stage of learning and then using full guidance at intermediate/advanced learning stages seemed to be the most effective. This implies that partial haptic guidance is more effective to learn the gross shape of handwriting skills (at early stages of the learning process) whereas full haptic guidance is more effective to learn the fine details of the handwriting skills (at intermediate or advanced stage of learning). Therefore, partial-then-full haptic guidance seems to be the most effective to improve learning outcomes.