Summary of "Dasar-dasar pengukuran gelombang otak dengan electroencephalography (EEG)"
Summary of "Dasar-dasar pengukuran gelombang otak dengan Electroencephalography (EEG)"
This video provides a comprehensive introduction to the basics of measuring brain waves using Electroencephalography (EEG). It covers fundamental concepts, the technology behind EEG, brain wave types, data acquisition, noise/artifacts, and data analysis methods, concluding with practical tools and references for further study.
Main Ideas and Concepts
1. Introduction to EEG and Brain Waves
- EEG is a tool to measure brain waves by detecting electrical potentials generated by neurons.
- The brain produces several types of waves: Delta, Theta, Alpha, Beta, and Gamma.
- Alpha waves are associated with relaxation or sleep.
- Beta waves relate to active thinking.
- Gamma waves relate to concentration.
- These classifications are simplified models and have underlying assumptions.
2. What is EEG?
- EEG records voltage differences on the scalp using metal Electrodes.
- Electrodes pick up electrical potentials generated by brain activity.
- EEG data (electroencephalogram) is represented graphically.
- EEG is a form of functional brain imaging, focusing on brain activity rather than structure.
3. Comparison with Other Brain Imaging Methods
- EEG has excellent temporal resolution (milliseconds to sub-milliseconds), allowing immediate detection of brain activity.
- fMRI has better spatial resolution (can localize activity to specific brain regions) but slower temporal resolution (seconds).
- EEG records signals from millions of synchronized neurons, mainly pyramidal cells in the cerebral cortex.
- Single-cell or synapse-level imaging requires invasive methods not applicable to humans.
4. Neural Basis of EEG Signals
- EEG measures postsynaptic potentials (PSPs) of pyramidal neurons, not individual action potentials.
- Pyramidal cells are oriented perpendicular to the cortical surface, influencing the direction of electrical and magnetic fields.
- EEG measures electrical potentials (aligned with cell orientation), while MEG measures magnetic fields (perpendicular to electrical fields).
5. Limitations of EEG
- Due to the folded brain structure (gyri and sulci), electrical signals recorded at an electrode may originate from distant brain areas.
- EEG cannot precisely localize brain activity sources.
- Analogy: recording crowd noise outside a stadium—you hear the noise but not individual speakers.
6. EEG Equipment and Setup
- Components: Electrodes (metal disks), Electrode caps, Amplifiers, and computers.
- Amplifiers boost tiny brain signals and convert analog signals into digital data.
- Sampling rate is crucial: higher sampling rates (e.g., 1000 Hz or more) better capture brain wave details and avoid aliasing errors.
- Electrodes types:
- Wet Electrodes use conductive gel for better contact.
- Dry Electrodes (comb-like) are easier to apply but may provide lower-quality data.
- Passive Electrodes send raw signals to Amplifiers.
- Active Electrodes have built-in Amplifiers for better signal quality.
- Electrode placement follows the 10-20 system, a standardized method using anatomical landmarks (nasion, inion, ear points) to ensure consistent electrode positioning.
7. Brain Wave Analysis
- Brain waves are complex mixtures of multiple sine waves at different frequencies and amplitudes.
- Raw EEG signals are a superposition of multiple frequencies (Delta to Gamma).
- To identify specific wave types, signal decomposition (e.g., Fourier Transform) is used.
- Types of analysis:
- Time domain (e.g., event-related potentials or ERPs): looks at wave patterns relative to stimulus timing.
- Frequency domain: identifies power in specific frequency bands (Alpha, Beta, etc.).
- Time-frequency analysis: combines both aspects to show how frequencies change over time.
- Machine learning can be applied to detect hidden features in EEG data.
8. Noise and Artifacts in EEG
- EEG signals are very small and easily contaminated by noise.
- Common noise sources:
- Electrical noise from power lines (50 Hz in Indonesia, 60 Hz in the US).
- Eye movements and blinks produce large artifacts detected near frontal Electrodes (EOG: electrooculogram).
- Muscle activity (EMG), e.g., jaw clenching, swallowing, causes high-frequency noise.
- Electrode movement or poor contact causes channel noise.
- Alpha waves can be considered noise if participants are sleepy during experiments.
- Cardiac signals (ECG) and blood pulsations can also contaminate EEG.
- Noise mitigation strategies:
- Minimize electrical devices in the room.
- Use Faraday cages to shield external electromagnetic interference.
- Ensure proper electrode placement and gel application.
- Instruct participants to minimize movement and blinking.
- Data cleaning and filtering during analysis.
9. Data Processing and Interpretation
Raw EEG
Category
Educational
