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Brain Machine Interface Shipments to Exceed 25 Million Per Annum by 2030, as Consumer Market Flourishes

New data from Juniper Research has found that the global number of shipments of BMIs (Brain Machine Interfaces); devices that connect computers to the brain, will reach 25.6 million by 2030, up from an estimated 350,000 in 2019.

According to the new research, Brain Machine Interfaces & Neuromodulation: Impact Assessments, Vendor Positioning & Market Forecasts 2019-2030, BMIs will expand beyond pure experimental medical use cases, with strong consumer uses developing. BMIs will offer the ability to control virtual reality scenarios; enhancing the user experience and immersion level. Consumer BMI devices will also enable compelling wellness functions, such as supporting guided meditation and enhancing sleep quality.

Juniper anticipates that this enhanced consumer demand will drive consumer BMI shipments to almost 13 million per annum by 2030, from just over 100,000 in 2019.

Medical BMIs Will Drive Revenues

The research also found that while shipments of medical BMIs will be significantly lower, this segment will drive revenues throughout the forecast period; accounting for 78% of total revenues in 2030. Medical devices, such as experimental visual and limb prosthetics, will be highly expensive, with extensive research and clinical trials required in order to reach their full potential.

Research author Nick Maynard explained: “While other areas will drive shipments, the potential to change lives with medical BMIs is boundless. The race to get to market at scale is on, with clinical trials set to ramp up rapidly”.

EEG (Electroencephalography) the Dominant Interface for Now

Meanwhile, of all the potential interface technologies, EEG will remain the most dominant. While EEG is highly susceptible to noise, it is both affordable, given its existing prevalence in medical fields, as well as non-invasive, which is crucial for consumer adoption.

The research found that, in the longer term, the utilisation of machine learning will be crucial to improving the quality of EEG performance, which will reduce calibration time and unlock EEG’s full potential as a consumer control interface.


  • Editorial Director of the The Fintech Times

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