Gehirn-Computer-Interfaces 2025-2045: Technologien, Akteure, Prognosen
Ein Ausblick auf die Brain-Computer-Interface-Technologie als Werkzeug für die Schnittstelle zwischen Mensch und Maschine mit Anwendungen auf dem Forschungsmarkt, dem medizinischen Markt, dem Markt für assistierende Technologien und sogar auf dem Markt für Unterhaltungselektronik.
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This report characterizes the brain computer interface market, technologies, and players. This includes coverage across non-invasive and invasive technologies, comparisons across key technical benchmarks, and market forecasts from 2025 to 2045. This research includes 20 company profiles, including coverage of Neuralink and Blackrock Neurotech. The report leverages IDTechEx's decades of experience researching healthcare and consumer electronics markets. There is an analysis of the opportunities within assistive technology markets, as well as the broader wearable technology markets, specifically the augmented and virtual reality headset market. This report reveals an evolving opportunity for both non-invasive and invasive technologies across the next twenty years, with the overall brain computer interface market forecast to grow to over US$1.6bn by 2045.
Brain computer interfaces can decode neural signals for the control of electronic devices. Electrodes or sensors can be placed either on the surface of the head, within the skull, or even within the brain itself to record brain activity. When connected with data acquisition systems and the right software, brain computer interfaces can offer an alternative form of human machine interfacing. This technology has the potential to revolutionize the quality of life for people living with spinal cord injury, quadriplegia, and other motor and speech function impairments.
Non-invasive technology approaches include electroencephalography (EEG), functional near infrared spectroscopy (fNIRS) and now even wearable magnetoencephalography (MEG). Whilst non-invasive devices have an established market for brain monitoring, their potential for brain computer interfacing is yet to be realized. However, as the technology continues to improve, so does data processing techniques. This is complimented by an increased consumer acceptance of head-worn wearables such hearables and AR/VR headsets. This report analyses the corresponding scale of market opportunity this could bring, alongside the challenges and emerging competitor technologies including electromyography (EMG), eye tracking and hand tracking.
Hype around invasive brain computer interfaces remains high. High profile companies such as Neuralink and Blackrock Neurotech have gained more public attention as trials in human patients begin, and funding levels dramatically increase. Yet there are more players seeking to enter this market than many realize, competing on levels of invasiveness, biocompatibility, system complexity and time to market. This report outlines the distinct approaches of each, alongside detailed company profiles.
Overall, this report not only details the major technology approaches for brain computer interfacing, but also critically compares the applications, competition, remaining challenges and corresponding market outlook for each. This is alongside coverage of supply chain innovations impacting this market, including coverage of wearables sensors such as dry-electrodes, photo-detectors and magnetic field sensors including quantum sensors.
Key Aspects of the Report
This report provides critical market intelligence about the brain computer interface market and each of the competing technologies targeting the research, healthcare, consumer and assistive technology markets. This includes:
A review of the context of the technology behind brain computer interfacing innovations
- History and context of EEG, fNIRS, MEG, ECoG and other invasive approaches including the Utah Array.
- Benchmarking and analysis of BCI technology performance including by spatial resolution, temporal resolution, invasiveness, signal to noise ratio, penetration depth and cost.
- General overview of the state of adoption of BCI technologies for human machine interfacing (HMI) applications.
- SWOT Analysis of each BCI technology.
- Multiple technology roadmaps assessing opportunities by material, integrated device form-factor and applications.
Market characterization for brain computer interface technology in the short, medium and long term
- Identification of key platers across the brain computer interface market eco system. This includes coverage of key electrode developers and manufacturers, established and emerging device integrators/OEMs and prominent start-ups and scale-ups disrupting the market.
- Review of the funding and patent landscape across multiple brain computer interfacing technologies.
- Detailed overview of emerging competitor technologies to brain computer interfaces, including a dedicated section on interfaces for extended reality devices covering eye-tracking and hand-tracking. Comparisons are also made to other wearables solutions, notably EMG enabled wrist-worn devices and mouthpads.
Market analysis throughout
- Reviews of key brain computer interface market players, with 20 company profiles included with this report.
- Market forecasts from 2025-2045 segmented by both technology (non-invasive and invasive) and target market (research, medical, consumer and assistive).
| Report Metrics | Details |
|---|---|
| CAGR | The Brain-computer interface market is forecast to surpass US$1.6B in 2045, representing a CAGR of 8.4% since 2025. |
| Forecast Period | 2025 - 2045 |
| Forecast Units | Annual Revenue (USD) |
| Segments Covered | Non-Invasive Technology Market (Research/Medical) Non-Invasive Technology Market (Consumer) Non-Invasive Technology Market (Assistive) Invasive Technology Market (Research) Invasive Technology Market (Assistive) Invasive Technology Market (Consumer) |
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| 1. | EXECUTIVE SUMMARY |
| 1.1. | Brain Computer Interfaces - Overview of Report Contents |
| 1.2. | Brain computer interfaces: introduction, report scope and major applications |
| 1.3. | BCIs can broadly be categorized as invasive and non-invasive |
| 1.4. | Market Map of Key Players Developing BCI Technologies with HMI Applications |
| 1.5. | Overview of the competitive landscape for brain computer interfaces as human machine interfaces |
| 1.6. | Drivers for emerging alternative human machine interfaces |
| 1.7. | Emerging human machine interfacing solutions competing with BCIs |
| 1.8. | Overview of the measurement principles of BCI technologies |
| 1.9. | Comparing key benchmarks and performance criteria of BCI technology |
| 1.10. | State of adoption of BCI technologies for HMI applications |
| 1.11. | An opportunity for EEG based BCI in virtual reality? |
| 1.12. | Big-Tech and EEG for BCI |
| 1.13. | SWOT analysis of dry electrodes for EEG and the consumer electronics market |
| 1.14. | Summary and outlook for wearable EEG in BCI applications |
| 1.15. | Main conclusions: Outlook for EEG and Dry Electrodes |
| 1.16. | Comparing fNIRS to other non-invasive brain imaging methods |
| 1.17. | fNIRS: SWOT analysis |
| 1.18. | Summary and outlook for wearable fNIRS in BCI applications |
| 1.19. | Background and context of MEG |
| 1.20. | Major barrier to adoption of MEG for BCI: Shielded Environments |
| 1.21. | Summary and outlook for MEG in BCI applications |
| 1.22. | Invasive neural interfaces: background and context |
| 1.23. | Future trends in invasive neural interface technology development |
| 1.24. | Founding and funding timelines of invasive BCI companies (2012-2024) |
| 1.25. | Key conclusions on invasive brain computer interface technologies |
| 1.26. | SWOT Analysis: Commercial applications of brain computer interfaces |
| 1.27. | Brain-computer interface technology: overall twenty-year market forecast by annual revenue (US$ Millions) |
| 1.28. | The brain computer interface market 'at a glance' |
| 2. | INTRODUCTION |
| 2.1. | Chapter overview |
| 2.2. | Neurons, action potentials and local field potentials (LFPs) |
| 2.3. | Neural interface technology approaches |
| 2.4. | Sensorimotor cortex brain rhythms and their relationship with intentions |
| 2.5. | Overview of the measurement principles of BCI technologies |
| 2.6. | Introducing the role of spatial and temporal resolution in BCIs |
| 2.7. | The relationship between brain structure and BCI hardware penetration depth |
| 2.8. | Comparing key benchmarks and performance criteria of BCI technology |
| 2.9. | Comparing key benchmarks and performance criteria of BCI technology |
| 2.10. | Pros and Cons of Non-invasive Interfaces |
| 2.11. | Pros and Cons of Invasive Interfaces |
| 2.12. | Neurofeedback and brain computer interfacing are distinct but complimentary |
| 2.13. | State of adoption of BCI technologies for HMI applications |
| 2.14. | Market Map of Key Players Developing BCI Technologies with HMI Applications |
| 2.15. | Current and future trends in invasive and non-invasive neural interface technology development |
| 2.16. | Business model considerations: Consumables, reusables and the demand for increased hardware longevity |
| 2.17. | Trends in neurotechnology data acquisition |
| 2.18. | Regulators play a key role bringing brain computer interface technology to market |
| 2.19. | State of the industry: Patent analysis suggests filing numbers have peaked |
| 2.20. | Top 20 assignees for "brain computer interface" patents |
| 2.21. | Comparing patent application trends by BCI technology type |
| 2.22. | Clinical trials with public records remain limited in size |
| 2.23. | The impact of the US NIH BRAIN Initiative |
| 2.24. | Founding and funding timelines of invasive BCI companies (2012-2024) |
| 2.25. | Funding landscape of invasive BCI players - 2024 |
| 3. | ELECTROENCEPHALOGRAPHY (EEG) |
| 3.1. | Introduction to Electroencephalography (EEG) |
| 3.1.1. | Background and context of EEG for brain computer interfaces |
| 3.1.2. | Introduction to electroencephalography (EEG) measurements |
| 3.1.3. | Components of an EEG electrophysiology recording system |
| 3.1.4. | EEG is established, but BCI applications face continued challenges |
| 3.1.5. | The established implementation and application: Electrode caps in a clinical setting for neurological disease diagnosis or traumatic brain injury assessment |
| 3.1.6. | Wider market perspectives: Wearable EEG for sleep monitoring as wellness |
| 3.1.7. | Wider market perspectives: Wearable EEG for emotional state monitoring |
| 3.2. | Dry electrode innovations |
| 3.2.1. | Barriers to wider EEG adoption: Wet electrodes create a pain point |
| 3.2.2. | Comparing properties of wet and dry electrodes |
| 3.2.3. | Dry electrodes: A more durable emerging solution for multiple wearable technologies, where EEG is relatively niche |
| 3.2.4. | Key requirements of wearable electrodes |
| 3.2.5. | Key players in wearable electrodes in e-textiles, skin patches and watches |
| 3.2.6. | Material innovations in dry electrodes for EEG |
| 3.2.7. | Active electrode requirements for dry EEG |
| 3.2.8. | Dry electrodes for EEG |
| 3.2.9. | Main conclusions: EEG and Dry Electrodes |
| 3.3. | Key players and market trends in EEG for BCI |
| 3.3.1. | Wearable EEG is relatively established in the medical space, with BCI not currently a key target market for the biggest players |
| 3.3.2. | Device level integration of EEG for BCI applications: Form-factors and key players using dry electrodes |
| 3.3.3. | Comparing the size of key players offering EEG integrated products |
| 3.3.4. | Founding timelines of non-invasive BCI companies (2012-2024) |
| 3.3.5. | An opportunity for EEG based BCI in virtual reality |
| 3.3.6. | Barriers to wider EEG for BCI adoption: The form-factor advantage vs the channel count compromise |
| 3.3.7. | Patent analysis: EEG as an input arrangement for BCI (IPC G06F3/01) |
| 3.3.8. | Big-Tech and EEG for BCI |
| 3.3.9. | Hearable EEG for seizure prediction seeks FDA approval |
| 3.3.10. | Brain controlled wheelchairs (BCWs) using EEG prevalent in academic research, but not commercialized |
| 3.3.11. | Summary and outlook for wearable EEG in BCI applications |
| 4. | FUNCTIONAL NEAR INFRARED SPECTROSCOPY (FNIRS) |
| 4.1. | Overview of fNIRS technology and key players |
| 4.1.1. | Background and context of functional near infrared spectroscopy (fNIRS) |
| 4.1.2. | Basic principles of fNIRS (1) |
| 4.1.3. | Basic principles of fNIRS (2) |
| 4.1.4. | fNIRS: Disruption or coexistence with EEG? |
| 4.1.5. | Key players in fNIRS |
| 4.1.6. | NIRS application areas, BCI in context |
| 4.1.7. | How can fNIRS be utilized for brain computer interfacing |
| 4.2. | Photodetector innovations with fNIRS applications |
| 4.2.1. | PIN photodiode |
| 4.2.2. | Avalanche photodiode (APD) |
| 4.2.3. | Single-photon avalanche diodes |
| 4.2.4. | Silicon photomultiplier |
| 4.2.5. | SPAD vs SiPM |
| 4.2.6. | Comparison of common photodetectors |
| 4.2.7. | Major photodetector players |
| 4.3. | Summary and market outlook for fNIRS based BCI |
| 4.3.1. | Comparing fNIRS to other non-invasive brain imaging methods |
| 4.3.2. | fNIRS: SWOT analysis |
| 4.3.3. | Summary and outlook for wearable fNIRS in BCI applications |
| 5. | MAGNETOENCEPHALOGRAPHY (MEG) |
| 5.1. | Introduction to Magnetoencephalography (MEG) |
| 5.1.1. | Background and context of MEG |
| 5.1.2. | Basic Principles of MEG |
| 5.2. | Sensor innovations for MEG |
| 5.2.1. | Introduction: Quantifying magnetic fields |
| 5.2.2. | Sensitivity is key to the value proposition for quantum magnetic field sensors |
| 5.2.3. | Classifying magnetic field sensor hardware |
| 5.2.4. | High sensitivity applications in healthcare are quantum computing are key market opportunities for quantum magnetic field sensors |
| 5.2.5. | Superconducting Quantum Interference Devices (SQUIDs) |
| 5.2.6. | Applications of SQUIDs |
| 5.2.7. | Operating principle of SQUIDs |
| 5.2.8. | SQUID fabrication services are offered by specialist foundries |
| 5.2.9. | Key players in commercial applications of SQUIDs including MEG |
| 5.2.10. | Comparing key players with SQUID intellectual property (IP) |
| 5.2.11. | SQUIDs: SWOT analysis |
| 5.2.12. | Optically Pumped Magnetometers (OPMs) |
| 5.2.13. | Operating principles of Optically Pumped Magnetometers (OPMs) |
| 5.2.14. | Applications of optically pumped magnetometers (OPMs) (1) |
| 5.2.15. | Applications of optically pumped magnetometers (OPMs) (2) |
| 5.2.16. | MEMS manufacturing techniques and non-magnetic sensor packages key for miniaturized optically pumped magnetometers |
| 5.2.17. | Comparing key players with OPM intellectual property (IP) |
| 5.2.18. | Comparing the technology approaches of key players developing miniaturized OPMs for healthcare |
| 5.2.19. | OPMs: SWOT analysis |
| 5.2.20. | N-V center magnetic field sensors |
| 5.2.21. | Introduction to N-V center magnetic field sensors |
| 5.2.22. | Operating Principles of N-V Centers magnetic field sensors |
| 5.2.23. | Applications of N-V center magnetic field centers |
| 5.2.24. | Comparing key players in N-V center magnetic field sensor development |
| 5.2.25. | N-V Center Magnetic Field Sensors: SWOT analysis |
| 5.3. | Sector overview: MEG for BCI |
| 5.3.1. | Market opportunities for quantum magnetic field sensors in biomagnetic imaging |
| 5.3.2. | Case Study: Cerca Magnetics |
| 5.3.3. | Case Study: Bosch Quantum Sensing |
| 5.3.4. | Assessing the performance of magnetic field sensors |
| 5.3.5. | Comparing minimum detectable field and SWaP characteristics |
| 5.3.6. | Quantum Magnetometers: Sector Roadmap |
| 5.3.7. | Major barrier to adoption of MEG for BCI: Shielded Environments |
| 5.3.8. | Summary and outlook for MEG in BCI applications |
| 6. | INVASIVE NEURAL INTERFACES FOR BCI |
| 6.1. | Introduction to invasive neural interfaces |
| 6.1.1. | Invasive neural interfaces: Background and context |
| 6.1.2. | Examples of neural electrodes |
| 6.1.3. | Introduction to ECoG |
| 6.1.4. | Overview of LFP waveforms |
| 6.1.5. | How neural probes are typically made |
| 6.1.6. | Pros and Cons of select implanted probe materials |
| 6.1.7. | Considerations for electrode material selection |
| 6.1.8. | Considerations for insulating materials |
| 6.2. | Invasive BCI innovations and key players |
| 6.2.1. | Founding and funding timelines of invasive BCI companies (2012-2024) |
| 6.2.2. | Funding landscape of invasive BCI players - 2024 |
| 6.2.3. | What are development trends in research? |
| 6.2.4. | Blackrock Neurotech - Technology overview |
| 6.2.5. | Blackrock Neurotech - Recent research success for BCI applications |
| 6.2.6. | Blackrock Neurotech - Technology challenges |
| 6.2.7. | Utah Array 2.0 - The Utah Optrode Array |
| 6.2.8. | Neuralink - Technology overview |
| 6.2.9. | Neuralink - Commercialization depends on more successful human trials |
| 6.2.10. | Neuralink - Technology challenges ahead |
| 6.2.11. | Onward Medical - Technology overview (1) |
| 6.2.12. | Onward Medical - Technology overview (2) |
| 6.2.13. | Onward Medical - Seeking to commercialize multiple product lines in the next 1-5 years |
| 6.2.14. | Synchron - Technology overview |
| 6.2.15. | Synchron - More human trials ahead |
| 6.2.16. | Paradromics - Technology overview |
| 6.2.17. | Paradromics - Preparing to begin in-human trials |
| 6.2.18. | CorTec - Technology overview |
| 6.2.19. | Precision - Bidirectional flexible arrays |
| 6.2.20. | Inbrain Neuroelectronics - Bidirectional graphene-based arrays |
| 6.2.21. | NeuroXess - Silktrodes and Surftrodes |
| 6.2.22. | Axoft - A new player looking to compete on electrode biocompatibility and softness |
| 6.2.23. | Braingrade - A focus on BCIs for Alzheimer's |
| 6.2.24. | Key conclusions |
| 7. | KEY COMPETITOR TECHNOLOGIES FOR BRAIN COMPUTER INTERFACES |
| 7.1. | Overview of competitor technologies |
| 7.1.1. | Overview of the competitive landscape for brain computer interfaces as human machine interfaces |
| 7.1.2. | Drivers for emerging alternative human machine interfaces |
| 7.1.3. | Emerging human machine interfacing solutions competing with BCIs |
| 7.2. | Electromyography (EMG) and gesture control |
| 7.2.1. | Introduction to Electromyography (EMG) |
| 7.2.2. | Investment in EMG for virtual reality and neural interfacing is increasing |
| 7.2.3. | Investment in EMG for virtual reality and neural interfacing is increasing |
| 7.2.4. | Consumer trends: Smart-straps could take control in the meta-verse |
| 7.3. | Interfacing with AR/VR - eye tracking and hand tracking |
| 7.3.1. | What are VR, AR, MR and XR? |
| 7.3.2. | Controllers and sensing connect XR devices to the environment and the user |
| 7.3.3. | Beyond positional tracking: What else might XR headsets track? |
| 7.3.4. | Where are XR sensors located? |
| 7.3.5. | Sensors case study: Microsoft's HoloLens 2 |
| 7.3.6. | 3D imaging and motion capture |
| 7.3.7. | Application example: Motion capture in animation |
| 7.3.8. | Stereoscopic vision |
| 7.3.9. | Time of Flight (ToF) cameras for depth sensing |
| 7.3.10. | Structured light |
| 7.3.11. | Comparison of 3D imaging technologies |
| 7.3.12. | Microsoft: From Kinect to HoloLens |
| 7.3.13. | Intel's RealSense™: Structured light for 3D motion tracking vs. stereoscopic cameras |
| 7.3.14. | Summary: Positional and motion tracking for XR |
| 7.3.15. | Why is eye tracking important for AR/VR devices? |
| 7.3.16. | Eye tracking sensor categories |
| 7.3.17. | Eye tracking using cameras with machine vision |
| 7.3.18. | Eye tracking companies based on conventional/NIR cameras and machine vision software |
| 7.3.19. | Event-based vision for AR/VR eye tracking |
| 7.3.20. | Eye tracking with laser scanning MEMS |
| 7.3.21. | AdHawk Microsystems: Laser scanning MEMS for eye tracking |
| 7.3.22. | Capacitive sensing of eye movement |
| 7.3.23. | Summary: Eye tracking for XR |
| 7.3.24. | Other novel HMI interfaces |
| 7.3.25. | In-ear muscles could enable the next revolution in brain computer interfacing |
| 7.3.26. | Mouthpad utilizes the tongue as an 'eleventh finger' |
| 8. | MARKET FORECASTS AND APPLICATIONS |
| 8.1. | BCI - Commercial Applications Overview |
| 8.2. | Industry 5.0 and future mobility applications of brain computer interfaces? |
| 8.3. | Commercial status of BCI applications in 2024 |
| 8.4. | SWOT Analysis: Commercial applications of brain computer interfaces |
| 8.5. | Market Forecasts: Scope and methodology |
| 8.6. | Brain computer interface technology: Overall twenty-year market forecast by annual revenue (US$ Millions) |
| 8.7. | Brain computer interface technology: Twenty-year market share forecast by annual revenue |
| 8.8. | Non-invasive brain computer interface technology: Overall twenty-year market forecast by annual revenue (US$ Millions) |
| 8.9. | Invasive brain computer interface technology: Overall twenty-year market forecast by annual revenue (US$ Millions) |
| 8.10. | The brain computer interface market 'at a glance' |
| 9. | COMPANY PROFILES |
| 9.1. | Artinis Medical Systems |
| 9.2. | Axoft |
| 9.3. | Blackrock Neurotech |
| 9.4. | BrainCo — Brain EEG Headband and Robotic Prosthetic Hand |
| 9.5. | Braingrade |
| 9.6. | Cerca Magnetics |
| 9.7. | CorTec-Neuro |
| 9.8. | Datwyler (Dry Electrodes) |
| 9.9. | EarSwitch |
| 9.10. | IDUN Technologies |
| 9.11. | Kokoon |
| 9.12. | Naox Technologies |
| 9.13. | Neuralink |
| 9.14. | NeuroFusion |
| 9.15. | Onward Medical |
| 9.16. | Precision Neuroscience |
| 9.17. | Synchron |
| 9.18. | uCat |
| 9.19. | Wearable Devices Ltd. |
| 9.20. | Wisear |
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