The technology industry continues to be a hotbed of innovation, with activity driven by the rapid emergence and widespread adoption of game-changing technologies such as artificial intelligence (AI), and growing importance of technologies such as electroencephalography (EEG), electrocorticography (ECoG), machine learning, signal processing, functional magnetic resonance imaging (fMRI), and brain-machine interface system. The integration of AI technology in brain-machine interface enhances the efficiency and precision of translating neural signals into actionable commands, enabling individuals with physical limitations to regain control and independence. In the last three years alone, there have been over 3.6 million patents filed and granted in the technology industry, according to GlobalData’s report on Innovation in Artificial Intelligence: Brain-machine interface. Buy the report here.
However, not all innovations are equal and nor do they follow a constant upward trend. Instead, their evolution takes the form of an S-shaped curve that reflects their typical lifecycle from early emergence to accelerating adoption, before finally stabilising and reaching maturity.
Identifying where a particular innovation is on this journey, especially those that are in the emerging and accelerating stages, is essential for understanding their current level of adoption and the likely future trajectory and impact they will have.
300+ innovations will shape the technology industry
According to GlobalData’s Technology Foresights, which plots the S-curve for the technology industry using innovation intensity models built on over 2.5 million patents, there are 300+ innovation areas that will shape the future of the industry.
Within the emerging innovation stage, finite element simulation, ML-enabled blockchain networks and generative adverserial network (GAN) are disruptive technologies that are in the early stages of application and should be tracked closely. Demand forecasting applications, intelligent embedded systems and deep reinforcement learning are some of the accelerating innovation areas, where adoption has been steadily increasing. Among maturing innovation areas are wearable physiological monitors and smart lighting, which are now well established in the industry.
Innovation S-curve for artificial intelligence in the technology industry
Brain-machine interface is a key innovation area in artificial intelligence
A brain-machine interface (BMI) serves as a direct conduit for communication between the human brain and an external apparatus, enabling individuals to manipulate external devices, such as robotic arms or computers, through their cognitive processes. It is alternatively referred to as a neural-machine interface, a brain-computer interface, a direct neural interface, or a brain-machine interface system.
GlobalData’s analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 30+ companies, spanning technology vendors, established technology companies, and up-and-coming start-ups engaged in the development and application of brain-machine interface.
Key players in brain-machine interface – a disruptive innovation in the technology industry
‘Application diversity’ measures the number of different applications identified for each relevant patent and broadly splits companies into either ‘niche’ or ‘diversified’ innovators.
‘Geographic reach’ refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from ‘global’ to ‘local’.
Patent volumes related to brain-machine interface
Source: GlobalData Patent Analytics
Meta Platforms is a leading patent filer in brain-machine interface. The company’s patents are aimed at describing a method and system of inferring user intent based on neuromuscular signals. The system comprises a plurality of sensors configured to continuously record a plurality of neuromuscular signals from a user and at least one computer processor programmed to provide as input to a trained statistical model, the plurality of neuromuscular signals or information based on the plurality of neuromuscular signals.
The processor can also predict, based on an output of the trained statistical model, whether an onset of a motor action will occur within a threshold amount of time. The system then sends a control signal to at least one device based, at least in part, on the output probability, wherein the control signal is sent to at least one device prior to completion of the motor action by the user.
Other prominent patent filers in the brain-machine interface space include Xperi and Samsung Group.
In terms of geographic reach, BrainPatch leads the pack, followed by Coapt and Razer. In terms of application diversity, Neurolutions holds the top position, followed by Xperi and InnerEye.
Brain-machine interfaces can continuously learn and adapt to an individual's unique neural patterns, paving the way for improved accuracy, speed, and personalised interaction between the brain and external devices, revolutionising the possibilities for neuroprosthetics and rehabilitation. To further understand how artificial intelligence is disrupting the technology industry, access GlobalData’s latest thematic research report on Artificial Intelligence (AI) – Thematic Intelligence.