Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a lockedin state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain- Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements

Formulation of a novel HRV classification model as a surrogate fraudulence detection schema

Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%

Graphene-based flexible circuit on cotton fabric using wax patterning method

With recent development in the field of wearable devices for biomedical applications, various studies have been conducted on the fabrication of electrically conductive circuit on flexible substrate materials such as paper or textile. In this project, we propose the fabrication of electrically conductive circuit on cotton fabric using simple wax patterning method. Using this method, hydrophilic and hydrophobic regions were patterned on the fabric and graphene-poly (3,4-ethylenedioxythiophene): poly (styrenesulfonic acid) (graphene-PEDOT:PSS) ink was deposited on the hydrophilic region using pipetting method. Conductive lines with higher conductance were fabricated by multiple deposition of the conductive ink and electronic components were successfully attached on the fabric to develop a simple fully functional flexible circuit