use SSVEPs to detect and interpret the patient's intentions; this access method will be discussed later in this article. ANATOMY OF BCI Overall, the anatomy of a BCI is complex and involves multiple components that need to work together seamlessly to provide a reliable and effective means of communication or control of an external device. BCI systems involve a combination of hard- ware and software components. The image below demonstrates the process and how these systems work together to identify a person’s visual intentions and how they are captured and then turned into a language output system. The anatomy of a BCI can be broken down into several key parts, including: • Brain signal acquisition: This component involves the use of sensors or electrodes to capture signals from the brain. There are several types of sensors that can be used for this purpose, including electroencephalography (EEG), magne- toencephalography (MEG), and functional magnetic reso- nance imaging (fMRI). • Signal processing: The signals captured by the sensors need to be processed and analyzed in order to extract meaningful information. Signal processing algorithms can be used to filter out noise, enhance signal quality, and ex- tract features that are relevant to the task at hand. • Feature extraction and selection: This component involves the identification of specific features or patterns in the brain signals that are relevant to the intended application of the BCI. For example, in a BCI designed for communi-
These visual signals create corresponding electrical signals in the patient’s occipital lobe "visual evoked potentials" (SSVEPs) which are then identified via EEG. SSVEP (Steady State Visual Evoked Potentials) stands for Steady-State Visually Evoked Po- tential, it is a type of brainwave activity that occurs in response to visual flickering stimuli at a specific frequency. SSVEP is a com- mon non-invasive method used in current neuroscience and cognitive research to study visual processing and attention. SSVEPs are electrical signals that are generated in the oc- cipital lobe in response to visual stimuli. By presenting the ALS patient with a sequence of letters or words on a screen and monitoring the SSVEPs that are generated in response, the BCI can determine which letter or word the patient is thinking of or mentally fixating on. With this technology, it is now possible to
Anatomy of a BCI (Brain Computer Interface)
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