Interface Prototype for Interaction between Human and Medical Instrumentation based on Electroencephalograph (EEG) signal
Electroencephalograph (EEG) is a brain electrical signals measurement system with EEG signals as the result of this measurement system. Electrical signals from the brain actually are the result of electrical activity on the neuron. EEG signal are usually used for clinical applications to diagnose brain and mental condition from the patient. Since EEG signal which contain various information, many researchers is continuously learned to reveal any information that we can achieve and how to use the information and developing new technology that can be benefit for human beings.
Researchers begin to notice the possibility to use EEG signal as the source of information that can be used instantly as an interface between human and a certain device. This can be done because all activities that people made in a conscious condition related with EEG signal characteristic. One concept that became the focus right now is a direct usage of EEG signal to control or communicate with a certain device without passing motor nerve channels and muscles that widely known as Brain Computer Interface (BCI).
BCI development makes it possible to create special interface in the future that allocating patient with disability on motor nerve system where the brain can’t deliver the signal to control muscle movements, especially related to body parts movements. This condition can be suffered by the patient because of stroke, wrecked part of brain or caused by accident that forced patient to have amputation on the body part. BCI development makes it possible to do direct interaction between patient and medical rehabilitation devices, such as prostheses and wheel chair, or between human and computer, such as finding information and playing game The main objective of this research is to develop prototype the main elements for BCI application. On Figure 1 can be seen a framework of medical rehabilitation with BCI that electrode used to convert the patient’s brain activity condition into electrical signal with a very
small potential. Because of that, a bio-amplifier is needed to amplify certain electrical signal that represents brain activity to make it available for other steps of signal processing. Signal processing on BCI has some steps from first processing, characteristic extraction and pattern classification. Outcome from BCI used to create signal control to medical rehabilitation device.
Figure 1. Framework of Medical Rehabilitation Device with BCI
For hardware, we continue development of low cost bio-amplifier based on open-EEG project an a helm sensor to easier EEG electrodes setup on the scalp. This hardware is required to measure EEG signal. For software, we must choose an open source platform that easier to develop and to integrate with EEG hardware. The software must have features to communication with low cost bio-amplifier and several signal processing that required for BCI applications.
For basic application of BCI, the research was planned on two years. On the first year, further study to EEG signal characteristic related with specific event and motor cortex was done. On the first setups, the characteristic of ERP related with source of stimulus and the detail measurement of latency and amplitude of brain response was performed. One of the prominent purposes using ERP is brain-computer interface, which primary uses a P300 component. The aim of this study is to know how the visual cortex of human brain is responding to the primary color stimulation. Evoked potential components such as P300 became the parameter to evaluate the particular pattern of visual cortex’s responses by each of color stimulation. The results shown that visual cortex of all subjects’ responses varies during the stimulation of red, green, and blue. P300 and N200 are found as dominant evoked potential components during stimulations. Both components also statistically varies on latency and amplitude in each color as well as in electrode locations, indicates each color stimulates a specific pattern in subjects’ visual cortex, mainly in P300 and N200. On the second experimental setup, the particular EEG signal that related with movement Imagery process will be investigated based on brain wave – Mu rhythm. Mu rhythm, which has 8-13 Hz frequency interval, has long been known particularly arise in brain motor cortex. It also has been used as one of several parameters in BCI research. How the mu rhythm is appropriately stimulated and analyzed is a crucial question in developing the BCI system based on mu rhythm. This study aims to evaluate the method for trigger and extract the mu rhythm as an EEG signal feature. Four electrodes were used and placed in electrode locations C3, C4, Oz, and Fz to record the brain signal during real motor activity and imagining the motor activity. EEG signal feature was then extracted using power spectral density (PSD) analysis to see the power dominance and significance of the frequencies arose by stimulation. The results show power significant changes and power dominance at 8-13 Hz frequency interval in C3 and C4, for both real motor activity and imagining the motor activity. This result shows a possibility in utilizing imagining process of motor activity to stimulate the mu rhythm as the command input for BCI.
On the second year, starting off from Mu Rhythm phenomenon explained previously, in this work we are conducting experiment in acquiring EEG signal related to My Rhythm activity. In short, we are trying to process acquired raw EEG data so that Mu Wave signal feature can be extracted, then we use it to fed classification algorithm, to find distinctive parameter of the two classes/types of the mental task. Then finally in the last step, we put our BCI system to the test by using the classification algorithm online altogether with real-time EEG acquisition. In order to increase information delivery performance from subject’s brain to BCI system, experiments are divided into three stages (see Figure 2).
Figure 2. three stages of the experiment
First is EEG data collection, which is Mu wave. Data is then processed to extract band power as the main feature of the signal, and afterwards used as input for the learning process in classification algorithm using Linear Discriminant Analysis (LDA). And the last step is to put the trained classification algorithm into testing phase. The making of complete Brain-Computer Interface system is possible by using open source products: ModularEEG hardware and OpenViBE software. From the study case, we find two brain wave patterns, associated with left and right hand movement imagination, can be recognized with accuracy between 50% – 100%. With linear classification algorithm, the use of Brain-Computer Interface for left-right hand movement imagining activity can not be done with single trial. Different system parameter is needed for every experiment session to get the best recognition accuracy.
Further characteristic of ERP related with source of stimulus and the detail measurement of latency and amplitude of brain response was performed. One of the prominent purposes using ERP is brain-computer interface, which primary uses a P300 component. The aim of this study is to know how Attention Response Study on Human Cortex by Electroencephalograph using Single Stimulus Evoked Potential Analysis. In this experiment, three-stimulus oddball paradigm was used to elicit Event-related Potential (ERP) components with visual stimuli. Data taken from EEG were processed using single stimulus wavelet analysis to get ERP components. ERP components are characterized by the amplitude and latency. The result of this experiment showed that three-stimulus paradigm is able to elicit P3a and P3b. The amplitude peak occurs in Cz for P3a while the highest amplitude occurs in Pz for P3b. These results match the previous study which suggested that attention shifting information occur from frontal area to parietal area.
LIST OF RESEARCH OUTPUT
1. Aiandy Yoga P. B., Suprijanto, Ayu Gareta R, Farida I. Muchtadi, Implementation Study of Open Source Systems for Brain-Computer Interface Basic Platform to Recognize Hand Movement Imagination The 11th Seminar on Intelligent Technology and Its Applications (SITIA) 2010
2. Ulfa Octaviani, Lulu L. Fitri, Suprijanto, Attention Response Study on Human Cortex by Electroencephalograph using Single Stimulus Evoked Potential Analysis, The Third International Conference on Mathematics and Natural Sciences (ICMNS) 2010 (The Best Paper Presentation on the Health Science)
3. Indrawan Tanjung, S.T. Pengenalan Pola Sinyal EEG menggunakan Model Markov Tersembunyi untuk Aplikasi Antar Muka Otak-Komputer berbasis P300, Thesis S2 Magister Instrumentasi dan Kontrol (Supervisor : Dr. Farida I. Muchtadi and Dr. Suprijanto) Implementation of the open source software OpenVibe (http://openvibe.inria.fr) for test and use Brain-Computer Interfaces. The figure of prototype implementation shown in Figure 3,4 and 5.
Figure 3. Open EEG design from 2 Channels and upgrade version on 4 channels
Figure 4. The experiment setups of BCI on laboratory of medical Instrumentation. Note (A). Subject with electrodes installed on the scalp in the specific location, (B). Two channels Bio-amplifier based on Open EEG project (C) Define target (D) Implementation on open source software OpenVibe to recognize cursor command to move right and left
Figure 5. Screen capture of the basic programming on open source software OpenVibe to recognize cursor command to move right and left
HEAD OF RESEARCH TEAM : Dr. Suprijanto ST MT
TEAM MEMBERS : Dr. Farida I. Muchtadi
OFFICIAL ADDRESS : Gedung Lab. Tek. VI Lt. II, Medical Instrumentation laboratory
Jl. Ganesha No. 10 Bandung 40132, Telp. (022) 2504424
ext. 231, Fax. (022) 2506281