cation, the relevant features might be associated with the intention to move a specific muscle or focus attention on a particular visual stimulus. • Classification: Classification is the technical term to de- scribe the way machines (i.e., computers) group things into meaningful categories. Once the relevant features have been identified, a classifier can be trained to recognize and interpret them. Machine learning algorithms are common- ly used for this purpose and can be trained to recognize specific patterns in the data that correspond to different intended actions or states. • Feedback and control: The final component of a BCI is the feedback and control mechanism. This involves providing feedback to the user based on their brain signals and using those signals to control a device or perform a specific ac- tion. The feedback can take the form of visual, auditory, or tactile cues, and the control mechanism can be anything from a robotic arm to a computer cursor, virtual keyboard or AAC (Augmentative and Alternative Communication) device. This complete BCI feedback system translates the patient's intentions into commands that can be used to select the de- sired letter, word, phrase, or access command on the screen. A brain-controlled interface can allow the patient to communicate without the need for physical movement or speech, which can be difficult if not impossible for patients with advanced ALS. BCI technology works when a person views a visual flashing stimulus, such as a flickering light, the brain generates electri- cal activity that can be detected using occipital lobe electroen- cephalography (EEG). In the case of SSVEP, the stimulus flickers at a fixed frequency, typically between 5 and 60 Hz. The brain's electrical activity synchronizes with the frequency of the stimu- lus, resulting in a characteristic pattern of brainwave oscillations at that frequency. These responses can be detected as distinct peaks or frequency components in the EEG signal. By analyzing the amplitude, phase, and other characteristics of these com- ponents pALS will then be able to activate language options or other Internet of things (IoT). Below is an example of a partici- pant from a Cognixion study being exposed to a stimulus at 7Hz and responding accurately via EEG.
Historically, SSVEPs have had a wide range of applications dedicated to laboratory settings. They are commonly used in neuroscience research to investigate visual perception, atten- tion, and cognitive processes. It has also found applications in brain-computer interfaces (BCIs), where users can control ex- ternal devices or interact with computer systems using their brain activity. The robust and reliable nature of SSVEP responses makes them useful for designing efficient and accurate BCIs. THE FUTURE IS NOW FOR SGD’S AND BCI Cognixion has taken on the challenge to provide patients with ALS (pALS) with access to language throughout the disease progression with pure BCI access. With many pALS, their access to their SGD via eye tracking, switch, or touch becomes difficult if not impossible due to the progression in the late stages. This is not due to the lack of technical success of their current technol- ogy but more of an issue with lack of bodily access, again their eye gaze failing. BCI access is a quite different access method than eye track- ing. With eye tracking, one must physically look and track to- wards the desired stimuli using the eye and the camera system of the tracking system (with some desired acceptance input such as a “dwell” option). With BCI as an access method, one must visually gaze (mentally fixate) at the flashing stimuli, thus triggering the SSVEP. This mental fixation will then activate the desired acceptance of what the individual is accessing. With electrodes attached to the occipital lobe, Cognixion’s BCI SGD with EEG stimuli presented can associate letters, words, phrases, or symbols to language (see below). This means that stimuli will be presented to the person allowing them to men- tally fixate on letters, numbers, words and/or phrases allowing them to continue to communicate when other access methods have failed. This innovative system will allow individuals with an inability to functionally eye-track to still have access to language (see image below).
TRAINING AND RETRAINING BCIs often require some form of training and retraining for the user. For example, in a BCI designed for communication, the user may need to undergo training to learn how to control
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