EEG Simulation Laboratory

An interactive learning environment for undergraduate electrical engineering students

Explore EEG theory, simulate brain signals, apply signal processing, and classify mental states

EEG Theory and Fundamentals

What is Electroencephalography (EEG)?

Electroencephalography (EEG) is a non-invasive electrophysiological monitoring method to record electrical activity of the brain. It measures voltage fluctuations resulting from ionic current within the neurons of the brain.

EEG Signal Characteristics

EEG signals are typically in the range of 0.5-100 μV and can be categorized into frequency bands that correlate with different mental states:

Delta Waves (0.5-4 Hz)

Associated with deep sleep and unconsciousness

Theta Waves (4-8 Hz)

Linked to drowsiness, meditation, and creative states

Alpha Waves (8-13 Hz)

Present during relaxed, wakeful states with closed eyes

Beta Waves (13-30 Hz)

Associated with active thinking, focus, and alertness

Gamma Waves (30-100 Hz)

Related to higher cognitive processing and information integration

EEG Electrode Placement

The international 10-20 system is the standard for electrode placement. It ensures reproducible positioning across subjects and studies. Electrodes are placed at sites corresponding to underlying brain regions (frontal, temporal, parietal, occipital).

Applications of EEG

EEG is used clinically to diagnose epilepsy, sleep disorders, encephalopathies, and brain death. In research, it's used in cognitive neuroscience, brain-computer interfaces, and neurofeedback.

EEG Signal Generation

Adjust parameters to simulate different brain wave patterns and mental states.

Alpha Waves (8-13 Hz): 0.5

Beta Waves (13-30 Hz): 0.3

Theta Waves (4-8 Hz): 0.1

Noise Level: 0.2

Current Signal Parameters

Dominant Frequency: 10.2 Hz

Amplitude Range: -45 to 45 μV

Signal-to-Noise Ratio: 12.4 dB

Simulated State: Relaxed Wakefulness

EEG Signal Processing

Apply digital filters to clean the EEG signal and remove artifacts.

Filter Type:

Filter Order: 4

Processing Results

Applied Filter: None

Noise Reduction: 0%

Artifact Removal: 0%

Processing Time: 0 ms

EEG Signal Classification

Classify mental states based on EEG signal features using a simple neural network.

Alpha Power (8-13 Hz): 0.5

Beta Power (13-30 Hz): 0.3

Theta Power (4-8 Hz): 0.1

Beta/Alpha Ratio: 0.6

Classification Result

No classification performed yet. Adjust parameters and click "Classify Mental State".

Confidence: 0%

Laboratory Report Structure

1. Introduction

Background: Explain what EEG is and its significance in biomedical engineering
Objectives: List specific learning objectives of the laboratory:
Scope: Briefly describe what the simulation covers and its limitations

2. Theoretical Background

Organize this section with clear subheadings as follows:

2.1 EEG Fundamentals

Explain the physiological basis of EEG signals
Describe the frequency bands (delta, theta, alpha, beta, gamma) and their associations
Discuss the international 10-20 electrode placement system

2.2 EEG Signal Characteristics

Typical amplitude and frequency ranges
Common artifacts and noise sources
Challenges in EEG signal acquisition

3.3 Signal Processing Techniques

Purpose of filtering in EEG analysis
Types of filters used (bandpass, notch, etc.)
Feature extraction methods for classification

3.4 Classification Approaches

Basic principles of pattern recognition in EEG
Common features used for mental state classification
Simple neural network concepts

4. Methodology

Describe what you did in the simulation as follows:

4.1 Simulation Setup

Explain the simulation environment (web-based, interactive controls)
List the adjustable parameters and their ranges

4.2 Experimental Procedure

For each laboratory activity (refer to the Experiment Notes):

Signal Generation: How you created different EEG patterns
Signal Processing: Filters applied and their parameters
Classification: Features used and classification process
Data Collection: How you recorded observations and results

4.3 Analysis Methods

How you interpreted signal visualizations
How you evaluated filter performance
How you assessed classification accuracy

5. Results and Analysis

Present your findings with appropriate figures, tables, and discussion.