Company: Neurable Inc.
Headquarters: Boston, Massachusetts, USA
Founded: 2017
Funding: 2 million (Series A)
Status: Private
Website: [neurable.com](https://www.neurable.com)
Overview
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...Company: Neurable Inc.
Headquarters: Boston, Massachusetts, USA
Founded: 2017
Funding: 2 million (Series A)
Status: Private
Website: [neurable.com](https://www.neurable.com)
Overview
Mermaid diagram (expand to render)
Neurable is a neurotechnology company developing brain-computer interface software that enables users to control digital devices using their thoughts. The company focuses on practical, accessible BCI technology that works with off-the-shelf hardware. Unlike many BCI companies that require invasive neural implants, Neurable's software-first approach uses consumer-grade EEG (electroencephalography) headsets, making the technology accessible to a broader audience["@neurable"].
The company's mission is to make brain-computer interfaces practical for everyday use, focusing on applications in cognitive enhancement, accessibility, and wellness. Neurable's approach represents a paradigm shift in the BCI field—rather than developing custom hardware, they leverage existing consumer EEG devices and apply advanced machine learning algorithms to decode neural signals["@wolpaw2000; @ramadan2017"].
Brain-Computer Interface Background
Brain-computer interfaces (BCIs) create direct communication pathways between the brain and external devices. The field has evolved significantly since early demonstrations in the 1970s[@vaughan2003]. BCIs can be classified by their signal acquisition method:
- Invasive: Require surgical implantation of electrode arrays (e.g., Neuralink, Blackrock Neurotech)
- Semi-invasive: Use electrodes placed on the surface of the brain (e.g., EcoG arrays)
- Non-invasive: Use external sensors like EEG or fMRI
Non-invasive EEG-based BCIs offer safety and accessibility but trade signal quality and spatial resolution. Recent advances in machine learning have substantially improved the accuracy of EEG-based intent detection, making consumer-grade BCIs viable for practical applications[@schalk2004; @krusienski2011].
Pipeline
Development Programs
| Program | Indication | Stage |
|---------|------------|-------|
| Development program | Research | Preclinical |
| Attention tracking | Consumer wellness | Commercial |
| Fatigue detection | Cognitive monitoring | Commercial |
| Thought control | Device control | Beta |
Software-First Approach
Neurable differentiates by focusing on software rather than custom hardware[@lebedev2006]:
- EEG-Based: Uses consumer-grade dry-electrode EEG headsets
- Machine Learning: Deep learning models for neural signal decoding
- Platform: SDK for developers to integrate BCI into applications
- Hardware Agnostic: Works with multiple consumer EEG devices
Key Capabilities
| Capability | Description | Status |
|------------|-------------|--------|
| Attention Tracking | Real-time engagement monitoring | Commercial |
| Thought Control | Intent detection for device control | Beta |
| Emotion Sensing | Affective computing integration | Research |
| Fatigue Detection | Cognitive load assessment | Commercial |
| Cognitive Assessment | Objective cognitive metrics | Research |
Technical Approach
Neurable's technology pipeline involves several key technical components[@mak2012]:
Signal Acquisition: Integration with consumer EEG headsets using standardized protocols
Preprocessing: Noise removal, artifact rejection, and signal filtering
Feature Extraction: Time-domain and frequency-domain feature calculation
Classification: Machine learning models (deep neural networks) for mental state decoding
Control Interface: SDK for application integrationThe company's machine learning models are trained on large datasets of neural recordings, enabling robust classification across users without extensive individual calibration—a significant advantage over earlier BCI systems that required lengthy calibration sessions[@birbaumer2006].
Products
MW75 Neuro Headphones
In partnership with Master & Dynamic, Neurable integrated brain sensing into premium headphones[@millar2010]:
- Sensors: Integrated EEG in ear cups with dry electrodes
- Features: Attention monitoring, focus sessions, cognitive load assessment
- Status: Commercial (2023)
- Use Cases: Productivity enhancement, meditation, cognitive training
This product represents one of the first commercially available consumer BCI devices integrated into everyday consumer electronics, rather than research or medical equipment.
- Neurable SDK: Software development kit for BCI integration
- Compatibility: Works with various EEG headsets (OpenBCI, Emotiv, Muse)
- Use Cases: Gaming, productivity, accessibility, research
- API: RESTful API for cloud-based signal processing
The developer platform enables third-party applications to leverage Neurable's neural decoding capabilities, potentially accelerating ecosystem development and application diversity.
Applications
Primary Use Cases
Gaming: Thought-based controls for immersive gaming experiences
Productivity: Focus enhancement and time management based on cognitive state
Accessibility: Hands-free computing for users with motor impairments
Research: Tool for neuroscience and cognitive psychology research
Wellness: Meditation and cognitive training applicationsRelevance to [Neurodegenerative](/diseases/neurodegeneration) Diseases
Neurable's BCI technology platform has significant emerging applications in neurodegenerative disease research and patient monitoring[@nichol2018; @ranganatha2017]:
[Alzheimer's Disease](/diseases/alzheimers-disease) and Dementia
Neurable's attention tracking and cognitive assessment capabilities have substantial applications in Alzheimer's disease research and early detection:
- Cognitive Assessment: Objective measures of attention, working memory, and executive function—domains affected early in AD
- Longitudinal Monitoring: Enable tracking of [cognitive decline](/mechanisms/cognitive-decline) over time outside clinical settings
- Early Detection: Changes in attention metrics may serve as early biomarkers for cognitive decline
- Treatment Monitoring: Objective measures for evaluating treatment efficacy in clinical trials
- Remote Monitoring: Enable at-home cognitive assessment, reducing burden on patients and caregivers
Research has demonstrated that EEG-based cognitive assessments can differentiate between cognitively normal individuals and those with mild [cognitive impairment](/mechanisms/cognitive-decline) or Alzheimer's disease, with specific patterns of slowing and dysconnectivity emerging in [neurodegeneration](/diseases/neurodegeneration).
[Parkinson's Disease](/diseases/parkinsons-disease)
The company's technologies have several relevant applications in PD:
- Fatigue Detection: Monitoring cognitive fatigue, a common non-motor symptom
- Movement Intention: Decoding movement-related neural signals for assistive devices
- Sleep Monitoring: REM sleep behavior disorder detection through EEG analysis
- Cognitive Assessment: Tracking PD-related cognitive impairment
- Deep Brain Stimulation Integration: Potential for closed-loop neuromodulation
Stroke Rehabilitation
BCI technology can support stroke recovery[@chao2010]:
- Motor Imagery: Training for motor rehabilitation through thought-based exercise
- Neuroplasticity Monitoring: Tracking recovery through neural signal changes
- Assistive Control: Enabling device control for patients with residual motor function
Clinical Research Applications
Neurable's technology enables:
- Virtual Clinical Trials: Remote cognitive assessment for clinical trials
- Naturalistic Monitoring: Data collection during daily activities
- Biomarker Discovery: Machine learning on large neural datasets for novel biomarkers
- Patient Stratification: Objective cognitive measures for trial enrollment
Key Differentiators
Neurable's competitive position rests on several factors:
Hardware Agnostic: Works with existing consumer EEG devices, lowering adoption barriers
Software Focus: Lower capital requirements than hardware-centric competitors
Practical Applications: Immediate use cases versus far-future vision
Consumer Focus: Mainstream consumer market versus exclusively medical applications
Rapid Iteration: Software updates can improve functionality without hardware changesCompetitive Landscape
The BCI field includes several distinct approaches:
| Company | Approach | Target Market |
|---------|----------|---------------|
| Neuralink | Invasive implants | Medical/consumer |
| Synchron | Stentrode (vascular) | Medical |
| Blackrock Neurotech | Invasive arrays | Research/medical |
| Paradromics | Invasive (high bandwidth) | Medical |
| Kernel | Non-invasive (ultrafast fNIRS) | Research/consumer |
| Muse | Consumer EEG | Wellness |
| Emotiv | Consumer EEG | Research/consumer |
| Neurable | Software-first EEG | Consumer/productivity |
Leadership
- Dr. Ramses Alcaide: CEO and co-founder (neuroscientist with expertise in neural engineering)
- Adam N. Perelman: Co-founder
- Scientific advisory board includes leading BCI researchers from major research institutions
External Links
- [Neurable Website](https://www.neurable.com/)
- [ClinicalTrials.gov](https://clinicaltrials.gov)
- [PubMed](https://pubmed.ncbi.nlm.nih.gov/)
References
[Neurable Official Website](https://www.neurable.com)
[Wolpaw et al., Brain-computer interfaces for communication and control (2000)](https://pubmed.ncbi.nlm.nih.gov/11040214/)
[Lebedev & Nicolelis, Brain-machine interfaces: past, present and future (2006)](https://pubmed.ncbi.nlm.nih.gov/17005022/)
[Ramadan & Vasilakos, Review on brain computer interface techniques (2017)](https://pubmed.ncbi.nlm.nih.gov/28717891/)
[Schalk et al., BCI2000: a general-purpose system for brain-computer interface research (2004)](https://pubmed.ncbi.nlm.nih.gov/15140667/)
[Vaughan et al., Brain-computer interface research at the Wadsworth Center (2003)](https://pubmed.ncbi.nlm.nih.gov/12948651/)
[Mak & Wolpaw, Clinical applications of neural control (2012)](https://pubmed.ncbi.nlm.nih.gov/22653902/)
[Birbaumer, Brain-computer interfaces: communication and restoration of movement (2006)](https://pubmed.ncbi.nlm.nih.gov/16785169/)
[Krusienski et al., Critical steps for a brain-computer interface (2011)](https://pubmed.ncbi.nlm.nih.gov/21951854/)
[Miller, Neural interfaces for sensory substitution (2010)](https://pubmed.ncbi.nlm.nih.gov/20823861/)
[Ranganatha & Sriram, BCI technologies for healthcare (2017)](https://pubmed.ncbi.nlm.nih.gov/29191406/)
[Nichol et al., EEG-based brain computer interfaces for motor rehabilitation (2018)](https://pubmed.ncbi.nlm.nih.gov/30151623/)
[Chao et al., Use of chronic neural implants (2010)](https://pubmed.ncbi.nlm.nih.gov/20158866/)