Embed Size (px)
Citation preview
A Brain Computer InterfaceCillian Brewitt and Ian Assom
Supervisor: Dr Gordon LightbodyDepartment of Electrical and Electronic Engineering, University College Cork, Ireland
4th UCC School of Engineering Industry & Open Day, 11th March 2016
1. Introduction
A brain computer interface (BCI) is a system that allows directinformation transfer between a human brain and an externaldevice.
The aim of this project was to design and implement twoproof of concept BCI systems. EEG data was processed usingwavelet transform and machine learning algorithm to create acommunication system and an alertness detection system.
5. Results
• Alertness detection system
o Computes the power in thealpha wave frequency band
o Self calibration
o System accurately detectswhether the user’s eyes areopen or closed
• P300 speller
o Characters flash randomly
o User focuses on a targetletter
o P300 response occurs whenthe target letter is flashed
o 3 letters/minute with 83%-89% accuracy for one user
3. Methodology
• Wavelet Transform, time-frequency domain signal processing method suitable for analyzing EEG.
𝐶𝑊𝑇𝑥𝜓𝜏, 𝑠 =
1
𝑠
−∞
∞
𝑥(𝑡)𝜓(𝑡 − 𝜏
𝑠) 𝑑𝑡
• Support vector machine (SVM) / Linear discriminant analysis (LDA) – machine learning used to adapt the system to the user’s brain.
Contact email: [email protected] [email protected]
6. Exploitation
• P300 speller - communication channelfor severely impaired patient (ALS)
• Monitor driver’s alertness during longjourney
Fig 4: Alertness detection GUI
Fig 2: Wavelet time- frequency detection of a P300 signal
2. Background Theory
• Emotiv Epoc low-cost EEG recording headset - 14electrodes with in built amplifier gain and bandpass filter –128 Hz sampling rate, data transmission via 2.4 GHz RF.
• EEG brain waves – spontaneous electrical impulsesresulting form brain activity over short period of time –delta, theta, alpha and beta waves.
Fig 1: Alpha brain waves (8-14 Hz)
Fig 3: Three feature SVMfor the P300 Speller
Data Acquisition
Display
PC
User
2nd order Butterworth Filter
0.2-15Hz
Window Averaging
Principle Component
Analysis
DiscreteWavelet
Transform
Feature Vector
Correlation with Stored
Signal
Shannon Entropy
Non-linearEnergy
Support Vector Machine
Character Grid
Character Prediction
Pre-Processing
Feature extraction
Classification
Fig 5: P300 event related potential
Fig 7: P300 speller GUI
7. Acknowledgement
The authors would line to thank both Dr. Gordon Lightbody andHilary Mansfield for their constant assistance and advice.
4. P300 Speller Block Diagram
Number
of training
characters
Test Subject 1 (Ian) Test Subject 2 (Cillian)
Correct character
prediction accuracy
Correct character prediction
accuracy
LSVM LDA LSVM LDA
15 (78 ± 5)% (86 ± 3)% (60 ± 3)% (61 ± 4)%
10 (73 ± 9)% (80 ± 3)% ( 43 ± 4 )% (53 ± 5 )%
Fig 6: P300 speller accuracy
User
