The principle
Neurons communicate with tiny electrical currents. When large groups fire together, the combined activity can be picked up by sensors on the scalp. That recording is an electroencephalogram (EEG). It’s dominated by rhythms at different frequencies:
These associations are tendencies across groups of people, not rules for any
single moment.
Consumer headbands
The Elata SDK supports the Muse family over Web Bluetooth:
Research-grade systems use 32 to 256 wet electrodes in a cap. A headband uses a
handful of dry sensors in fixed positions. That makes it comfortable and
affordable, and it limits what it can measure.
How the Elata SDK processes EEG
eeg-web runs in the browser using WebAssembly:
- Filtering and spectra: removes drift and line noise, and computes the frequency spectrum of each window
- Band powers: absolute, relative, and log power per band and channel
- Quality flags: flatline, clipping, extreme amplitude, and line noise, so bad windows can be excluded
- Simple models: for example, an alpha-rhythm detector and a calmness estimate built on band powers
What affects the signal
Studies comparing consumer headbands with research systems find that they can
capture real brain signals. Event-related potentials have been recovered with
Muse, and four Muse electrodes performed close to a 32-electrode dataset on
one emotion-classification task. But signal quality varies by device and
setup: one 2024 comparison found Muse S had the weakest alignment with a
research-grade amplifier of the devices tested.
What calm and focus scores can claim
A “calm” or “focus” score is a product interpretation built from band powers and other inputs. It is not a direct readout of a mental state.- Scores are most meaningful relative to the same person’s baseline, not compared between people.
- They should come with quality information and be withheld when the signal is poor.
- Some popular measures don’t hold up. For example, the SDK’s Focus score deliberately does not use the theta/beta ratio, because that ratio isn’t a valid measure of attention.
Sources
Peer-reviewed literature, retrieved via PubMed:- Krigolson OE, et al. Choosing MUSE: validation of a low-cost, portable EEG system for ERP research. Frontiers in Neuroscience, 2017. doi:10.3389/fnins.2017.00109
- Mikhaylov D, et al. Comparison of EEG signal spectral characteristics obtained with consumer- and research-grade devices. Sensors, 2024. doi:10.3390/s24248108
- Garcia-Moreno FM, et al. EEG headbands vs caps: how many electrodes do I need to detect emotions? The case of the MUSE headband. Computers in Biology and Medicine, 2024. doi:10.1016/j.compbiomed.2024.109463
eeg-web and biosignal-analytics (public SDK repository).