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.

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.

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.

Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real-life assistance to individuals facing movement challenges. However, the effectiveness of training with MI may vary depending on the location of the stroke lesion, which should be considered. This paper introduces a multi-task electroencephalogram-based heterogeneous ensemble learning (MEEG-HEL) specifically designed for cross-subject training. In the proposed framework, common spatial patterns were used for feature extraction, and the features according to stroke lesions are shared and selected through sequential forward floating selection. The heterogeneous ensembles were used as classifiers. Nine patients with chronic ischemic stroke participated, engaging in MI and motor execution (ME) paradigms involving finger tapping. The classification criteria for the multi-task were established in two ways, taking into account the characteristics of stroke patients. In the cross-subject session, the first involved a direction recognition task for two-handed classification, achieving a performance of 0.7419 (±0.0811) in MI and 0.7061 (±0.1270) in ME. The second task focused on motor assessment for lesion location, resulting in a performance of 0.7457 (±0.1317) in MI and 0.6791 (±0.1253) in ME. Comparing the specific-subject session, except for ME on the motor assessment task, performance on both tasks was significantly higher than the cross-subject session. Furthermore, classification performance was similar to or statistically higher in cross-subject sessions compared to baseline models. The proposed MEEG-HEL holds promise in improving the practicality of neurorehabilitation in clinical settings and facilitating the detection of lesions.

Handwriting is an essential skill for developing sensorimotor and intellectual skills in children. Handwriting constitutes a complex activity relying on cognitive, visual-motor, memory and linguistic abilities, and is therefore challenging to master, especially for children with learning difficulties such as those with cognitive, sensorimotor or memory deficits. Recently-emerged haptic guidance systems have a potential to facilitate the acquisition of handwriting skills in both adults and children. In this paper we present a longitudinal experimental study that examined the effects of haptic guidance to improve handwriting skills in children with cognitive and fine motor delays as a function of the handwriting complexity in terms of visual familiarity and haptic difficulty. A haptic-based handwriting training platform that provides haptic guidance along the trajectory of a handwriting task was utilized. 12 children with cognitive and fine motor delays defined in terms of intellectual difficulty (IQ score) and mild motor difficulty in pincer grasp control, participated in the study. Children were divided into two groups, a target group and a control group. The target group completed haptic-guided training and pencil-and-paper test whereas the control group took only the pencil-and-paper test without any training. A total of 32 handwriting tasks was used in the study where 16 tasks were used for training while the entire 32 tasks were completed for evaluation. Results demonstrated that the target group performed significantly better than the control group for handwriting tasks that are visually familiar but haptically difficult (Wilcoxon signed-rank test, p $<; $ 0.01). An improvement was also seen in the performance of untrained tasks as well as trained tasks (Spearman's linear correlation coefficient, 0.667; p $=$ 0.05). In addition to confirming that haptic guidance can significantly improve motor functions, this study revealed a significant effect of task difficulty (visual familiarity and haptic complexity) on the effectiveness of haptic guidance for handwriting skill acquisition for children with cognitive and fine motor delays.

Haptics technologies have the potential to considerably improve the acquisition of handwriting skills by providing physical assistance to improve movement accuracy and precision. To date, very few studies have thoroughly examined the effectiveness of various haptic guidance methods to leverage the acquisition of handwriting skills. In this paper, we examine the role of several methods for haptic guidance, namely full haptic guidance, partial haptic guidance, disturbance haptic guidance, and no-haptic guidance towards improving the learning outcomes of handwriting skills acquisition for typical children. A group of 42 children from Cranleigh School Abu Dhabi across two educational stages, namely Foundation Stage 2 (FS2, 4-5 years old) and Year 2 (6-7 years old), participated in this study. Results showed that disturbance haptic guidance was the most effective for high complexity handwriting tasks (such as writing the letters 'o' and 'g'), partial haptic guidance was the most effective for medium complexity handwriting tasks (such as 't', 'r', 's', 'e', 'n', 'a' and 'b'), and full haptic guidance was the most effective for low complexity letters (such as 'i'). Another interesting finding was that FS2 participants had statistically significant improvement in handwriting speed compared to Year 2 group, demonstrated by a significantly shorter test completion time. Furthermore, female children performed statistically better than their male counterparts in partial guidance. These results can be utilized to build more effective haptic-based handwriting tools for typical children.