The power cepstrum-based parameters for steady-state visually evoked potential (SSVEP) is proposed. To precisely represent the characteristics of frequency responses of a visually stimulated electroencephalography (EEG) signal, power cepstrum analysis is adopted to estimate the parameters in low-dimensional space. To represent the frequency responses of SSVEP, the log-magnitude spectrum of an EEG signal is estimated by fast Fourier transform. Subsequently, the discrete cosine transform is applied to linearly transform the log-magnitude spectrum into the cepstrum domain, and then generate a set of coefficients. Finally, a Bayesian decision model with a Gaussian mixture model is adopted to classify the responses of SSVEP. The experimental results demonstrated that the proposed approach was able to improve performance compared with previous approaches and was suitable for use in brain computer interface applications.

Touch is one most of the important aspects of human life. Nearly all interactions, when broken down, involve touch in one form or another. Recent advances in technology, particularly in the field of virtual reality, have led to increasing interest in the research of haptics. However, accurately capturing touch is still one of most difficult engineering challenges currently being faced. Recent advances in technology such as those found in microcontrollers which allow the creation of smaller sensors and feedback devices may provide the solution. Beyond capturing and measuring touch, replicating touch is also another unique challenge due to the complexity and sensitivity of the human skin. The development of flexible, soft-wearable devices, however, has allowed for the creating of feedback systems that conform to the human form factor with minimal loss of accuracy, thus presenting possible solutions and opportunities. Thus, in this review, the researchers aim to showcase the technologies currently being used in haptic feedback, and their strengths and limitations.

The means of assisting visually impaired and blind (VIB) people when travelling usually relies on other people. Assistive devices have been developed to assist in blind navigation, but many technologies require users to purchase more devices and they lack flexibility, thus making it inconvenient for VIB users. In this research, we made use of a mobile phone with a depth camera function for obstacle avoidance and object recognition. It includes a mobile application that is controlled using simple voice and gesture controls to assist in navigation. The proposed system gathers depth values from 23 coordinate points that are analyzed to determine whether an obstacle is present in the head area, torso area, or ground area, or is a full body obstacle. In order to provide a reliable warning system, the research detects outdoor objects within a distance of 1.6 m. Subsequently, the object detection function includes a unique interactable feature that enables interaction with the user and the device in finding indoor objects by providing an audio and vibration feedback, and users were able to locate their desired objects more than 80% of the time. In conclusion, a flexible and portable system was developed using a depth camera-enabled mobile phone for use in obstacle detection without the need to purchase additional hardware devices.

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

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