Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help message

Hand Gesture to Control Virtual Keyboard using Neural Network

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character

A brain computer interface based on FFT and multilayer neural network -feature extraction and generalization-

金沢大学理工研究域　電子情報学

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs