On the Use of Electrooculogram for Efficient Human Computer Interfaces

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as “clean-up” could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes

USB-based 256-channel electroencephalographic data acquisition system for electrical source imaging of the human brain

A 256-channel electroencephalographic data acquisition system is designed for electrical source imaging of the human brain. The system is microcontroller based, dc-coupled, battery powered, and uses a universal serial bus for communication. An analog to digital converter of 24-bit sigma-delta technology with a sampling rate of 140 Hz and the number of effective bits in conversion is 18, corresponding to a dynamic range of 108 dB. Data is transferred via USB at the rate of 1 Mbyte/sec. The common mode rejection ratio of the system is measured as 102 dB. The performance tests and recorded experimental data show that the developed system can be used in conducting human brain source localization experiments

A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases

An efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amiotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. Often, such diseases leave the ocular movements preserved for a relatively long time. The aim of this study is to present a new approach for the hybrid system which is based on the recognition of electrooculogram (EOG) and electroencephalogram (EEG) measurements for efficient communication and control. As a first step we show that the EOG-based side of the system for communication and controls is useful for patients. The EOG side of the system has been equipped with an interface including a speller to notify of messages. A comparison of the performance of the EOG-based system has been made with a BC! system that uses P300 waveforms. As a next step, we plan to integrate EOG and EEG sides. The final goal of the project is to realize a unique noninvasive device able to offer the patient the partial restoration of communication and control abilities with EOG and EEG signals