This thesis report has been submitted to the Department of Biomedical Engineering (BME), Khulna University of Engineering and Technology (KUET), Khulna-9203, for part. This is to prove that the thesis work entitled "Study of stimulation effects in different bands of the EEG signal" was supervised by Prof. Mohiuddin Ahmad, Professor Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering &.
This is to confirm that the thesis submitted by Tarun Kanti Ghosh entitled "Study of Stimulation Effects on Different Bands of EEG Signals" has been approved by the Board of Assessors in partial fulfillment of the requirements for the Master of Science in Engineering in the Department of Biomedical Engineering (BME ), Khulna University of Engineering & Technology (KUET), Khulna-9203, Bangladesh in September- 2016. Special thanks to those who acted as my subjects during data collection in the Biomedical Engineering Laboratory. The aim of the research is to control the different stimulation effects of the human brain, to improve the accuracy, higher information transfer rate (ITR), desired bandwidth (BW) and signal to noise ratio (SNR) of BCIs and to identify the Power and Energy of different stimulation effects on alpha and beta bands of EEG signal.
In this research, the power and energy of the alpha and beta bands of the EEG signal were determined for different stimulations, and signal analysis, signal processing, fast Fourier transform (FFT), statistical parameter methods were used. The information transfer rate (ITR) varies with changes in the frequency and magnitude of visual stimuli.
L IST OF S YMBOLS
- Origin of EEG Signal
- Medical and Research Applications of EEG
- Information from EEG
- Method of EEG
- Limitations of EEG
- Different Wave patterns of EGG
- Theoretical Background of EEG Signal
- Mathematical Representation of EEG Signal
- Factors affecting SSVEP based BCI
- Stimulation Type
- Stimulation Frequency
- Stimulation Color
Additional electrodes can be added to the standard setup when a clinical or research application requires increased spatial resolution for a particular part of the brain. ECoG is typically recorded at higher sampling rates than scalp EEG due to the requirements of Nyquist's theorem: the subdural signal consists of a higher predominance of higher frequency components. EEG is most sensitive to a particular set of post-synaptic potentials, which are generated in superficial layers of the cortex, at the tips of the gyri directly adjacent to the skull and radial to the skull.
However, neural backpropagation, as a typically longer dendritic current dipole, can be picked up by EEG electrodes and is a reliable indicator of the occurrence of neural output. Currents moving up or down, these processes underlie most of the signals produced by electroencephalography. The human brain is the part of the body that regulates almost all human behavioral activity.
It is believed that pyramidal neurons of the cortex produce the most EEG signal because they align well and fire together. For example, the strength of the SSVEP response is influenced by both the frequency and color of the stimuli [16].
1 .5. Problem Statement
Scope of the Research
This research will help detect different stimulation effects of human brain that are useful for medical diagnosis. This research will help to select perfect stimuli that can match the performance of SSVEP-based BCIs and safety of BCIs. This research will help to design the software for investigating the effect of frequency, color and different sizes of circular stimulator on the performance (power, energy and ITR).
Structure of this Thesis
- Related works
- Comparative Discussion
- Related works
- EEG Signal Recording Techniques 3.2. Proposed Block Diagrams
- Signal Extraction using Desired Condition 3.6. Signal Preprocessing
- Signal Analysis
- Energy and power calculation
- EEG Signal Recording Techniques
- Recording Electrodes
- Amplifiers and Filters
- Artefacts
- Proposed Block Diagrams
- Software & Hardware description
- Biopac Student Lab(BSL) Pro 3.7.3& BIOPAC MP36 acquisition unit
- BIOPAC MP36
- Brain Stimulation
- Effects of Brain Stimulation
- EEG Signal Extraction Using Desired Condition
- Signal Preprocessing
- Filtering for signal processing
- Feature Extraction
- Energy and Power Calculation
- Visual Frequency Generator Software 4.2. Design of Stimulator
- Subject Condition and Signal Extraction 4.4. Graphical Analysis
- Confusion Matrix as a Heat Map 4.6. Tabular Analysis
- Visual Frequency Generator Software
- Design of Stimulator
- Subject Condition and Signal Extraction
- Graphical Analysis
- Confusion Matrix as a Heat Map
- Tabular Analysis
- Conclusion
- Future Research
The EEG reflects the electrical activity of the brain, which can be recorded directly from the scalp in a non-invasive way using surface electrodes [11]. The properties of the SSVEP depend on the display unit and the frequency, size, shape and color of the stimuli. On the other hand, in this research, one of the vital factors of stimulation in BCIs (circular shape with different size, color and frequency) is briefly analyzed.
The amplifier must protect the patient against any risk of electric shock. The desired biopotential appears as the differential signal between the two input terminals of the differential amplifier. The common mode rejection ratio is the ratio of the differential mode gain (desired signal) to the common mode gain (original input signal between the inputs and ground).
The resolution of the converter is determined by the smallest amplitude that can be sampled. The width of the data points usually varies in the order of 1000 and one of the window functions must be selected. Noise can be determined by connecting the amplifier inputs together, or by immersing them in a saline solution, or.
Basic evaluation of the EEG traces includes scanning for signal distortions called artifacts. The main aim of the research is to determine the stimulating effect on alpha and beta bands of EEG signals. The BSL PRO software enables editing of data and controlling the way it appears on screen and performs four general functions: Controls the data acquisition process, performs real-time calculations (such as digital filtering and velocity detection), performs post-acquisition transformations (such as FFTs and mathematical functions), handles file handling commands (save, print, etc.) The heart of the BSL system is the MP3X data acquisition device.
Regardless of the type of connected device, each sensor or I/O device connects to the MP3X capture unit via the "Simple Sensor" connector. In this step, the raw EEG signal will be used as the input of the preprocessing unit. After selecting the format, the frequency and color are selected from the frequency and color option of the software.
Maximum power and energy is achieved at 2.5 inch diameter of the stimulator for green color stimulator. The variation of energy and power with respect to variation of frequency and size of the stimulator simultaneously is shown in fig. From table 4.2, for a fixed size and color of the stimulator, the power and energy of the alpha wave for different stimulation frequencies are found.
The change in power and energy of the Beta wave is exactly opposite to that of the Alpha wave as given in the table above.
R EFERENCES
Ahmad, "Design and Implementation of a User-Independent SSVEP-Based Brain-Computer Interface with High Transfer Rates." Ahmad, “Design and Simulation of Cost Effective Wireless EEG Acquisition System for Patient Monitoring”, International Conference on Informatics, Electronics and Vision (ICIEV) (ISBN Dhaka, Bangladesh. Attari, “Development of Wireless High Immunity EEG Recording System”, 2011 International Conference on Electronic Devices, Systems and Applications (ICEDSA).
Xu, “Steady State Visual Evoked Potentials Evoked by Multiple Color Stimuli,” IEEE Engineering in Medicine and Biology Society 23rd Annual International Conference, 2001. Collura, “Real-time filtering for estimation of steady-state visually evoked brain potentials,” IEEE Trans. Muchtadi, “Prototype design of a low-cost four-channel digital electroencephalograph for sleep monitoring”, 2nd International Conference on Instrumental Control and Automation, November 17-17, 2011, Bandung, Indonesia.
Gao, “Brain-computer interfaces based on visually evoked potential: feasibility of practical system designs,” IEEE Engineering in Medicine and Biology Magazine, vol. Birch, “A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals,” Journal of Neural Engineering, vol. On the blind source separation of the human electroencephalogram by estimated joint diagonalization of second-order statistics.
A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on target clinical variables: a simulation case. 34;Electrical stimulation of the human brain: perceptual and behavioral phenomena reported in the old and new literature". Lu, "Invariance of brain wave representations of simple visual images and their names", PNAS, vol.
ACHIEVMENT
International Journal
International Conference
