Clinical EEG devices are limited in their design and user interface. All clinical EEGs have a setup time between 30 minutes to 1 hour, due to the use of wet electrodes. Researchers and EEG technicians have to ensure that the scalp is well exposed by moving away hair. Electrodes have to be attached one by one onto a patient’s scalp and conductive gel has to be applied to ensure contact between the scalp and the electrodes. This procedure is very tedious and it may cause patients' discomfort. Additionally, the wires that connect to the EEG can cause additional signal noise and inconsistency in the data collection. Software associated with clinical EEGs does not have a comprehensive interface and requires in depth training to use.
The future of EEG devices lies within mobile EEGs with comprehensive, complementary software. There has been a large amount of innovation within the mobile EEG industry in the past few years with companies developing EEG devices for both consumer and research use. The new EEG devices are fully mobile and wireless with complementing software. Some use wet and others use dry electrodes. The electrodes of future EEG devices will be dry allowing for fast, consistent setup while maintaining high signal quality.
Due to the various applications of EEGs, ranging from brain computer interfaces to neuromarketing, specific channels will be required for different applications. Creating an EEG with 32 or 64 channels is too expensive for most consumer or research use. Therefore, a different device will be required depending on the application. For example, if you want to study attention in classrooms you would need electrode locations on FP1 and FP2 with algorithms that can infer attention from the user’s brainwaves. These and other uses create significant opportunities for both EEG and application developers and we will continue to see growth within the industry.
The struggle with current mobile EEG devices on the market is that signal quality and resolution is poor. Because of this, data collected is hard to interpret and algorithms are inaccurate. EEG devices must be able to solve this issue while still being affordable to consumers, businesses and researchers.
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