#neurotech #brain-computer-interface #bci #brain #cheatsheet Created at 120723 # [Anonymous feedback](https://www.admonymous.co/louis030195) # [[Epistemic status]] #shower-thought Last modified date: 120723 Commit: 0 # Related - [[Neurotech market]] - [[Brain computer interface]] # neurotech ### data from the [[Brain|brain]] | Data Type | Description | | --- | --- | | Electroencephalography (EEG) | Measures electrical activity in the brain using electrodes placed on the scalp. EEG can be used to detect brain waves associated with different mental states, such as relaxation, attention, and meditation. EEG is commonly used in BCIs to detect changes in brain activity associated with specific mental tasks. | | Magnetoencephalography (MEG) | Measures magnetic fields produced by electrical activity in the brain using sensors placed outside the scalp. MEG provides high temporal and spatial resolution and is used to study brain activity associated with sensory processing, language, and memory. | | Functional Magnetic Resonance Imaging (fMRI) | Measures changes in blood flow in the brain using magnetic fields and radio waves. fMRI provides high spatial resolution and is used to study brain activity associated with cognitive processes such as attention, memory, and decision-making. | | Positron Emission Tomography (PET) | Measures changes in blood flow and metabolism in the brain using radioactive tracers. PET is used to study brain activity associated with various mental states, including addiction, depression, and anxiety. | | Near-Infrared Spectroscopy (NIRS) | Measures changes in blood oxygenation in the brain using near-infrared light. NIRS provides a non-invasive method for measuring brain activity and is used in BCIs to detect changes in brain activity associated with specific mental tasks. | | Electrooculography (EOG) | Measures electrical activity in the muscles that control eye movements. EOG is used to detect eye movements and is often used in BCIs to control computer interfaces using eye movements. | | Electromyography (EMG) | Measures electrical activity in the muscles. EMG is used to detect muscle activity and is often used in BCIs to control computer interfaces using muscle movements. | This table provides a brief overview of different types of data that can be obtained from the brain using various techniques. EEG, MEG, fMRI, PET, and NIRS are all used to study brain activity associated with different mental states and cognitive processes. EOG and EMG are used to detect eye and muscle movements, respectively, and are often used in BCIs to control computer interfaces. ### eeg ![[Pasted image 20230720205306.png]] ![[Pasted image 20230720205239.png]] ### algorithms | Algorithm | Description | Application | | --- | --- | --- | | Common Spatial Patterns (CSP) | A spatial filtering technique that maximizes the difference in variances between two classes of EEG signals | Motor imagery classification | | Linear Discriminant Analysis (LDA) | A statistical method that finds a linear combination of features that separates two or more classes of data | Motor imagery classification | | Support Vector Machine (SVM) | A machine learning algorithm that finds a hyperplane in a high-dimensional space that maximally separates two classes of data | Motor imagery classification, P300 detection | | Convolutional Neural Network (CNN) | A deep learning architecture that uses convolutional layers to extract features from EEG signals | Motor imagery classification, P300 detection | | Recurrent Neural Network (RNN) | A deep learning architecture that uses recurrent layers to model temporal dependencies in EEG signals | EEG signal classification, decoding | | Hidden Markov Model (HMM) | A statistical model that represents a sequence of observations as a sequence of hidden states | EEG signal decoding, error correction | | Independent Component Analysis (ICA) | A blind source separation technique that separates a multichannel EEG signal into independent components | Artifact removal, feature extraction | | Wavelet Transform (WT) | A time-frequency analysis technique that decomposes an EEG signal into different frequency bands | Feature extraction, artifact removal | Note that this is not an exhaustive list of all algorithms used in BCI research, but rather a selection of commonly used algorithms. The table includes a brief description of each algorithm and its typical application in BCI research.