BCI Kickstarter #01 : Introduction to Brain-Computer Interfaces
Welcome to the first installment of our crash course on Brain-Computer Interfaces (BCIs)! This fascinating field holds immense potential for revolutionizing how we interact with technology and the world around us. In this series, we'll delve into the core concepts, methodologies, and applications of BCIs, providing you with a solid foundation to understand this exciting domain.
What is a BCI?
Brain-Computer Interfaces, or BCIs, are systems that establish a direct communication pathway between the brain and external devices. They bypass traditional neuromuscular pathways, allowing individuals to control machines or receive sensory feedback using only their brain activity. This groundbreaking technology has far-reaching implications for various fields, including healthcare, assistive technology, gaming, and even art.
At its core, a BCI system operates through three fundamental steps:
- Brain Signal Acquisition: Neural activity is recorded using various techniques, such as electroencephalography (EEG), electrocorticography (ECoG), or invasive electrode implants.
- Signal Processing: The acquired brain signals are processed and analyzed to extract meaningful patterns and features related to the user's intent or mental state.
- Output and Control: The extracted information is translated into commands that control external devices, such as prosthetic limbs, wheelchairs, or computer cursors.
BCIs offer a powerful means of communication and control, particularly for individuals with severe motor impairments or those suffering from locked-in syndrome. They open up a world of possibilities for restoring lost function, enhancing human capabilities, and even creating entirely new forms of human-computer interaction.
The Evolution of BCI over time
The history of brain-computer interfaces (BCIs) dates back to 1924 when Hans Berger first recorded human brain activity using electroencephalography (EEG). However, significant BCI research gained momentum in the 1970s at the University of California, Los Angeles (UCLA), focusing on using EEG signals for basic device control. Since then, advancements in the field have propelled BCI technology forward, including:
- Improved Electrodes and Sensors: Development of high-density electrode arrays and more sensitive sensors for better signal acquisition.
- Advanced Signal Processing Techniques: Sophisticated algorithms for filtering noise, artifact removal, and feature extraction from brain signals.
- Machine Learning Revolution: Application of machine learning algorithms for pattern recognition and classification, enabling more accurate decoding of user intent.
These advancements have led to impressive applications of BCIs:
- Restoring Lost Function: Controlling prosthetic limbs, wheelchairs, and communication devices for individuals with paralysis or locked-in syndrome.
- Assistive Technology: Developing tools for environmental control, such as controlling lights or appliances with brain signals.
- Gaming and Entertainment: Creating immersive and interactive experiences using brain-controlled interfaces.
- Neurofeedback and Therapy: Utilizing BCIs for treating conditions like ADHD, anxiety, and chronic pain.
- Cognitive Enhancement: Exploring the potential of BCIs for improving memory, attention, and other cognitive functions.
The future of BCIs is brimming with possibilities. As research progresses, we can expect even more groundbreaking applications that will further transform how we interact with the world and push the boundaries of human potential.
A Look at Different BCI Types
BCI systems are categorized based on their level of invasiveness, each with its own trade-offs in terms of signal quality, complexity, and risk. The three main types are:
Invasive BCI: Direct Access to the Source
Invasive BCIs involve surgically implanting electrodes directly into the brain tissue. This method offers the highest signal quality and allows for the most precise control. However, it also carries the highest risk of medical complications and requires significant expertise for implantation and maintenance.
Example: The Utah Array, a microelectrode array, is a prominent example of an invasive BCI used in research and clinical trials.
Applications: Primarily used for restoring lost motor function in individuals with paralysis or locked-in syndrome, enabling them to control prosthetic limbs, wheelchairs, and communication devices.
Semi-Invasive BCI (ECoG): Bridging the Gap
Semi-invasive BCIs, specifically Electrocorticography (ECoG), involve placing electrodes on the surface of the brain, beneath the skull. This approach offers a balance between signal quality and invasiveness, providing higher resolution than non-invasive methods while minimizing the risks associated with penetrating the brain tissue.
One notable company in this field is Synchron, which employs a minimally invasive endovascular procedure—similar to stent placement—to avoid open brain surgery. Their innovative approach allows for the safe and effective placement of electrodes, enabling patients to interact with technology using their thoughts. In a recent clinical trial, their first participant successfully connected with his wife by digitally controlling his computer through thought.
Applications: ECoG-based BCIs, including those developed by Synchron, are currently used in research settings for investigating brain function and exploring potential applications in epilepsy treatment and motor rehabilitation.
Non-Invasive BCIs: Exploring the Brain from the Outside
Non-invasive BCIs are the most common and accessible type, as they rely on external sensors to record brain activity without the need for surgery. These methods are generally safer and more comfortable for the user. However, the signal quality can be affected by noise and artifacts, requiring sophisticated signal processing techniques for accurate interpretation.
Examples:
- Electroencephalography (EEG): Measures electrical activity in the brain using electrodes placed on the scalp.
- Magnetoencephalography (MEG): Detects magnetic fields generated by brain activity.
- Functional Magnetic Resonance Imaging (fMRI): Measures brain activity by detecting changes in blood flow.
- Functional Near-Infrared Spectroscopy (fNIRS): Measures brain activity by monitoring changes in blood oxygenation
EEG-Based BCIs: A Deep Dive
Among the various non-invasive BCI approaches, electroencephalography (EEG) stands out as a particularly promising and widely adopted technology. EEG-based BCIs leverage the electrical activity generated by the brain, recorded through electrodes placed on the scalp. This method offers several key advantages:
High Temporal Resolution: EEG boasts excellent temporal resolution, capturing brain activity changes in milliseconds. This rapid sampling rate allows for real-time detection of subtle shifts in brain states, crucial for accurate and responsive BCI control.
Portability and Ease of Use: EEG systems are relatively portable and easy to set up, making them suitable for a variety of environments, from research labs to home settings. The non-invasive nature of EEG also contributes to its ease of use, as it doesn't require surgery or complex procedures.
Cost-Effectiveness: Compared to other neuroimaging techniques like fMRI or MEG, EEG is significantly more affordable. This accessibility makes it an attractive option for research, development, and widespread adoption of BCI technology.
Wide Range of Applications: EEG-based BCIs have demonstrated their versatility in a multitude of applications, including:
- Motor Imagery BCIs: Allow users to control devices by imagining specific movements.
- P300 Spellers: Enable users to spell words by focusing their attention on specific letters.
- Steady-State Visual Evoked Potential (SSVEP) BCIs: Utilize visual stimuli to elicit brain responses for control.
- Neurofeedback and Therapy: Provide real-time feedback on brain activity to help users learn to self-regulate their brain states for therapeutic purposes.
Addressing the Limitations:
The primary challenge lies in EEG’s lower spatial resolution compared to invasive or semi-invasive techniques. The electrical signals recorded by EEG electrodes are a mixture of activity from various brain regions, making it more difficult to pinpoint the precise source of the signal. However, advancements in signal processing and machine learning algorithms are continually improving the ability to extract meaningful information from EEG data, mitigating this limitation.
On the whole, EEG-based BCIs, especially when combined with fNIRS, offer a compelling mix of high temporal resolution, portability, affordability, and versatility. This combination enhances the ability to capture both electrical activity and hemodynamic responses in the brain, providing a more comprehensive understanding of brain function. These advantages have propelled EEG to the forefront of BCI research and development, driving innovation and expanding the potential applications of this transformative technology.
The Future is Brain-Powered
Brain-computer interfaces have emerged as a revolutionary technology with the potential to fundamentally change how we interact with the world around us. While various approaches exist, EEG-based BCIs stand out due to their unique combination of high temporal resolution, portability, cost-effectiveness, and versatility. From restoring lost motor function to enhancing cognitive abilities, the applications of EEG-based BCIs are vast and rapidly expanding.
As research and development continue to advance, we can expect even more groundbreaking innovations in the field of BCIs, leading to a future where our brains can seamlessly interact with technology, unlocking new possibilities for communication, control, and human potential.
Ready to Dive Deeper?
This concludes the first part of our crash course on Brain-Computer Interfaces. We've explored the fundamental concepts, different types of BCIs, and the advantages of EEG-based systems.
In the next installments of this series, we'll concern ourselves with specific aspects of BCI technology, covering topics such as:
- Fundamentals of Neuroscience for BCI: Understanding the brain's electrical activity and how it relates to BCI control.
- EEG Signal Acquisition and Processing: Exploring the techniques used to record and analyze EEG data.
- BCI Paradigms and Applications: Examining different types of BCI systems and their specific applications.
- Building Your Own BCI with Python: A hands-on guide to developing your own BCI applications.
Stay tuned for the next exciting chapter in our BCI journey!
Capturing a biosignal is only the beginning. The real challenge starts once those tiny electrical fluctuations from your brain, heart, or muscles are recorded. What do they mean? How do we clean, interpret, and translate them into something both the machine and eventually we can understand? In this blog, we move beyond sensors to the invisible layer of algorithms and analysis that turns raw biosignal data into insight. From filtering and feature extraction to machine learning and real-time interpretation, this is how your body’s electrical language becomes readable.
Every heartbeat, every blink, every neural spark produces a complex trace of electrical or mechanical activity. These traces known collectively as biosignals are the raw currency of human-body intelligence. But in their raw form they’re noisy, dynamic, and difficult to interpret.
The transformation from raw sensor output to interpreted understanding is what we call biosignal processing. It’s the foundation of modern neuro- and bio-technology, enabling everything from wearable health devices to brain-computer interfaces (BCIs).
The Journey: From Raw Signal to Insight
When a biosignal sensor records, it captures a continuous stream of data—voltage fluctuations (in EEG, ECG, EMG), optical intensity changes, or pressure variations.
But that stream is messy. It includes baseline drift, motion artefacts, impedance shifts as electrodes dry, physiological artefacts (eye blinks, swallowing, jaw tension), and environmental noise (mains hum, electromagnetic interference).
Processing converts this noise-ridden stream into usable information, brain rhythms, cardiac cycles, muscle commands, or stress patterns.
Stage 1: Pre-processing — Cleaning the Signal
Before we can make sense of the body’s signals, we must remove the noise.
- Filtering: Band-pass filters (typically 0.5–45 Hz for EEG) remove slow drift and high-frequency interference; notch filters suppress 50/60 Hz mains hum.
- Artifact removal: Independent Component Analysis (ICA) and regression remain the most common methods for removing eye-blink (EOG) and muscle (EMG) artefacts, though hybrid and deep learning–based techniques are becoming more popular for automated denoising.
- Segmentation / epoching: Continuous biosignals are divided into stable time segments—beat-based for ECG or fixed/event-locked windows for EEG (e.g., 250 ms–1 s)—to capture temporal and spectral features more reliably.
- Normalization & baseline correction: Normalization rescales signal amplitudes across channels or subjects, while baseline correction removes constant offsets or drift to align signals to a common reference.
Think of this stage as cleaning a lens: if you don’t remove the smudges, everything you see through it will be distorted.
Stage 2: Feature Extraction — Finding the Patterns
Once the signal is clean, we quantify its characteristics, features that encode physiological or cognitive states.
Physiological Grounding
- EEG: Arises from synchronized postsynaptic currents in cortical pyramidal neurons.
- EMG: Records summed action potentials from contracting muscle fibers.
- ECG: Reflects rhythmic depolarization of cardiac pacemaker (SA node) cells.
Time-domain Features
Mean, variance, RMS, and zero-crossing rate quantify signal amplitude and variability over time. In EMG, Mean Absolute Value (MAV) and Waveform Length (WL) reflect overall muscle activation and fatigue progression.
Frequency & Spectral Features
The power of each EEG band tends to vary systematically across mental states.
Time–Frequency & Non-Linear Features
Wavelet transforms or Empirical Mode Decomposition capture transient events. Entropy- and fractal-based measures reveal complexity, useful for fatigue or cognitive-load studies.
Spatial Features
For multi-channel EEG, spatial filters such as Common Spatial Patterns (CSP) isolate task-specific cortical sources.
Stage 3: Classification & Machine Learning — Teaching Machines to Read the Body
After feature extraction, machine-learning models map those features to outcomes: focused vs fatigued, gesture A vs gesture B, normal vs arrhythmic.
- Classical ML: SVM, LDA, Random Forest , effective for curated features.
- Deep Learning: CNNs, LSTMs, Graph CNNs , learn directly from raw or minimally processed data.
- Transfer Learning: Improves cross-subject performance by adapting pretrained networks.
- Edge Inference: Deploying compact models (TinyML, quantized CNNs) on embedded hardware to achieve < 10 ms latency.
This is where raw physiology becomes actionable intelligence.
Interpreting Results — Making Sense of the Numbers
A robust pipeline delivers meaning, not just data:
- Detecting stress or fatigue for adaptive feedback.
- Translating EEG patterns into commands for prosthetics or interfaces.
- Monitoring ECG spectral shifts to flag early arrhythmias.
- Quantifying EMG coordination for rehabilitation or athletic optimization.
Performance hinges on accuracy, latency, robustness, and interpretability, especially when outcomes influence safety-critical systems.
Challenges and Future Directions
Technical: Inter-subject variability, electrode drift, real-world noise, and limited labeled datasets still constrain accuracy.
Ethical / Explainability: As algorithms mediate more decisions, transparency and consent are non-negotiable.
Multimodal Fusion: Combining EEG + EMG + ECG data improves reliability but raises synchronization and power-processing challenges.
Edge AI & Context Awareness: The next frontier is continuous, low-latency interpretation that adapts to user state and environment in real time.
Final Thought
Capturing a biosignal is only half the story. What truly powers next-gen neurotech and human-aware systems is turning that signal into sense. From electrodes and photodiodes to filters and neural nets, each link in this chain brings us closer to devices that don’t just measure humans; they understand them.
Every thought, heartbeat, and muscle twitch leaves behind a signal, but how do we actually capture them? In this blog post, we explore the sensors that make biosignal measurement possible, from EEG and ECG electrodes to optical and biochemical interfaces, and what it takes to turn those signals into meaningful data.
When we think of sensors, we often imagine cameras, microphones, or temperature gauges. But some of the most fascinating sensors aren’t designed to measure the world, they’re designed to measure you.
These are biosignal sensors: tiny, precise, and increasingly powerful tools that decode the electrical whispers of your brain, heart, and muscles. They're the hidden layer enabling brain-computer interfaces, wearables, neurofeedback systems, and next-gen health diagnostics.
But how do they actually work? And what makes one sensor better than another?
Let’s break it down, from scalp to circuit board.
First, a Quick Recap: What Are Biosignals?
Biosignals are the body’s internal signals, electrical, optical, or chemical , that reflect brain activity, heart function, muscle movement, and more. If you’ve read our earlier post on biosignal types, you’ll know they’re the raw material for everything from brain-computer interfaces to biometric wearables.
In this blog, we shift focus to the devices and sensors that make it possible to detect these signals in the real world, and what it takes to do it well.
The Devices That Listen In: Biosignal Sensor Types
.webp)
A Closer Look: How These Sensors Work
1. EEG / ECG / EMG – Electrical Sensors
These measure voltage fluctuations at the skin surface, caused by underlying bioelectric activity.
It’s like trying to hear a whisper in a thunderstorm; brain and muscle signals are tiny, and will get buried under noise unless the electrodes make solid contact and the amplifier filters aggressively.
There are two key electrode types:
- Wet electrodes: Use conductive gel or Saline for better signal quality. Still the gold standard in labs.
- Dry electrodes: More practical for wearables but prone to motion artifacts and noise (due to higher electrode resistance).
Signal acquisition often involves differential recording and requires high common-mode rejection ratios (CMRR) to suppress environmental noise.
Fun Fact: Even blinking your eyes generates an EMG signal that can overwhelm EEG data. That’s why artifact rejection algorithms are critical in EEG-based systems.
2. Optical Sensors (PPG, fNIRS)
These use light to infer blood flow or oxygenation levels:
- PPG: Emits light into the skin and measures reflection, pulsatile blood flow alters absorption.
- fNIRS: Uses near-infrared light to differentiate oxygenated vs. deoxygenated hemoglobin in the cortex.
Example: Emerging wearable fNIRS systems like Kernel Flow and OpenBCI Galea are making brain oxygenation measurement accessible outside labs.
3. Galvanic Skin Response / EDA – Emotion’s Electrical Signature
GSR (also called electrodermal activity) sensors detect subtle changes in skin conductance caused by sweat gland activity, a direct output of sympathetic nervous system arousal. When you're stressed or emotionally engaged, your skin becomes more conductive, and GSR sensors pick that up.
These sensors apply a small voltage across two points on the skin and track resistance over time. They're widely used in emotion tracking, stress monitoring, and psychological research due to their simplicity and responsiveness.
Together, these sensors form the foundation of modern biosignal acquisition — but capturing clean signals isn’t just about what you use, it’s about how you use it.
How Signal Quality Is Preserved
Measurement is just step one; capturing clean, interpretable signals involves:
- Analog Front End (AFE): Amplifies low signals while rejecting noise.
- ADC: Converts continuous analog signals into digital data.
- Signal Conditioning: Filters out drift, DC offset, 50/60Hz noise.
- Artifact Removal: Eye blinks, jaw clenches, muscle twitches.
Hardware platforms like TI’s ADS1299 and Analog Devices’ MAX30003 are commonly used in EEG and ECG acquisition systems.
New Frontiers in Biosignal Measurement
- Textile Sensors: Smart clothing with embedded electrodes for long-term monitoring.
- Biochemical Sensors: Detect metabolites like lactate, glucose, or cortisol in sweat or saliva.
- Multimodal Systems: Combining EEG + EMG + IMU + PPG in unified setups to boost accuracy.
A recent study involving transradial amputees demonstrated that combining EEG and EMG signals via a transfer learning model increased classification accuracy by 2.5–4.3% compared to EEG-only models.
Other multimodal fusion approaches, such as combining EMG and force myography (FMG), have shown classification improvements of over 10% compared to EMG alone.
Why Should You Care?
Because how we measure determines what we understand, and what we can build.
Whether it's a mental wellness wearable, a prosthetic limb that responds to thought, or a personalized neurofeedback app, it all begins with signal integrity. Bad data means bad decisions. Good signals? They unlock new frontiers.
Final Thought
We’re entering an era where technology doesn’t just respond to clicks, it responds to cognition, physiology, and intent.
Biosignal sensors are the bridge. Understanding them isn’t just for engineers; it’s essential for anyone shaping the future of human-aware tech.
In our previous blog, we explored how biosignals serve as the body's internal language—electrical, mechanical, and chemical messages that allow us to understand and interface with our physiology. Among these, electrical biosignals are particularly important for understanding how our nervous system, muscles, and heart function in real time. In this article, we’ll take a closer look at three of the most widely used electrical biosignals—EEG, ECG, and EMG—and their growing role in neurotechnology, diagnostics, performance tracking, and human-computer interaction. If you're new to the concept of biosignals, you might want to check out our introductory blog for a foundational overview.
"The body is a machine, and we must understand its currents if we are to understand its functions."-Émil du Bois-Reymond, pioneer in electrophysiology.
Life, though rare in the universe, leaves behind unmistakable footprints—biosignals. These signals not only confirm the presence of life but also narrate what a living being is doing, feeling, or thinking. As technology advances, we are learning to listen to these whispers of biology. Whether it’s improving health, enhancing performance, or building Brain-Computer Interfaces (BCIs), understanding biosignals is key.
Among the most studied biosignals are:
- Electroencephalogram (EEG) – from the brain
- Electrocardiogram (ECG) – from the heart
- Electromyogram (EMG) – from muscles
- Galvanic Skin Response (GSR) – from skin conductance
These signals are foundational for biosignal processing, real-time monitoring, and interfacing the human body with machines. In this article we look at some of these biosignals and some fascinating stories behind them.
Electroencephalography (EEG): Listening to Brainwaves
In 1893, a 19 year old Hans Berger fell from a horse and had a near death experience. Little did he know that it would be a pivotal moment in the history of neurotechnology. The same day he received a telegram from his sister who was extremely concerned for him because she had a bad feeling. Hans Berger was convinced that this was due to the phenomenon of telepathy. After all, it was the age of radio waves, so why can’t there be “brain waves”? In his ensuing 30 year career telepathy was not established but in his pursuit, Berger became the first person to record brain waves.
When neurons fire together, they generate tiny electrical currents. These can be recorded using electrodes placed on the scalp (EEG), inside the skull (intracranial EEG), or directly on the brain (ElectroCorticogram). EEG signal processing is used not only to understand the brain’s rhythms but also in EEG-based BCI systems, allowing communication and control for people with paralysis. Event-Related Potentials (ERPs) and Local Field Potentials (LFPs) are specialized types of EEG signals that provide insights into how the brain responds to specific stimuli.
Electrocardiogram (ECG): The Rhythm of the Heart
The heart has its own internal clock which produces tiny electrical signals every time it beats. Each heartbeat starts with a small electrical impulse made by a special part of the heart called the sinoatrial (SA) node. This impulse spreads through the heart muscle and makes it contract, first the upper (atria) and then lower chambers (ventricles) – that’s what pumps blood. This process produces voltage changes, which can be recorded via electrodes on the skin.
This gives rise to the classic PQRST waveform, with each component representing a specific part of the heart’s cycle. Modern wearables and medical devices use ECG signal analysis to monitor heart health in real time.
Fun fact: The waveform starts with “P” because Willem Einthoven left room for earlier letters—just in case future scientists discovered pre-P waves! So, thanks to a cautious scientist, we have the quirky naming system we still follow today.
Electromyography (EMG): The Language of Movement
When we perform any kind of movement - lifting our arm, kicking our leg, smiling, blinking or even breathing- our brain sends electrical signals to our muscles telling them to contract. When these neurons, known as motor neurons fire they release electrical impulses that travel to the muscle, causing it to contract. This electrical impulse—called a motor unit action potential (MUAP)—is what we see as an EMG signal. So, every time we move, we are generating an EMG signal!
Medical Applications
Medically, EMG is used for monitoring muscle fatigue especially in rehabilitation settings and muscle recovery post-injury or surgery. This helps clinicians measure progress and optimize therapy. EMG can distinguish between voluntary and involuntary movements, making it useful in diagnosing neuromuscular disorders, assessing stroke recovery, spinal cord injuries, and motor control dysfunctions.
Performance and Sports Science
In sports science, EMG can tell us muscle-activation timing and quantify force output of muscle groups. These are important factors to measure performance improvement in any sport. The number of motor units recruited and the synergy between muscle groups, helps us capture “mind-muscle connection” and muscle memory. Such things which were previously spoken off in a figurative manner can be scientifically measured and quantified using EMG. By tracking these parameters we get a window into movement efficiency and athletic performance. EMG is also used for biofeedback training, enabling individuals to consciously correct poor movement habits or retrain specific muscles
Beyond medicine and sports, EMG is used for gesture recognition in AR/VR and gaming, silent speech detection via facial EMG, and next-gen prosthetics and wearable exosuits that respond to the user’s muscle signals. EMG can be used in brain-computer interfaces (BCIs), helping paralyzed individuals control digital devices or communicate through subtle muscle activity. EMG bridges the gap between physiology, behavior, and technology—making it a critical tool in healthcare, performance optimization, and human-machine interaction.
As biosignal processing becomes more refined and neurotech devices more accessible, we are moving toward a world where our body speaks—and machines understand. Whether it’s detecting the subtlest brainwaves, tracking a racing heart, or interpreting muscle commands, biosignals are becoming the foundation of the next digital revolution. One where technology doesn’t just respond, but understands.
