{"id":4379,"date":"2026-03-19T11:08:09","date_gmt":"2026-03-19T11:08:09","guid":{"rendered":"https:\/\/oraigo.com\/?p=4379"},"modified":"2026-03-19T11:08:12","modified_gmt":"2026-03-19T11:08:12","slug":"eeg-headbands-for-driver-fatigue-how-they-work","status":"publish","type":"post","link":"https:\/\/oraigo.com\/en\/eeg-headbands-for-driver-fatigue-how-they-work\/","title":{"rendered":"EEG Headbands for Driver Fatigue: How They Work"},"content":{"rendered":"\n

The Invisible Danger Behind the Wheel<\/strong><\/h2>\n\n\n\n

Every day, millions of drivers get behind the wheel carrying a risk they can’t see, measure, or easily control: fatigue.<\/p>\n\n\n\n

It doesn’t announce itself the way a mechanical failure does. There’s no warning light, no sudden noise, no obvious moment of breakdown. Instead, it creeps in gradually, a slightly slower reaction here, a fraction of a second of lost focus there, until the brain begins to shut itself down in short, involuntary bursts called microsleeps. These episodes can last anywhere from one to thirty seconds. At highway speed, that’s enough time to travel the length of several football fields with no one in control of the vehicle.<\/p>\n\n\n\n

This is precisely what an EEG headbands for driver fatigue<\/strong> does.<\/p>\n\n\n\n

The numbers behind this invisible danger are sobering. Research indicates that up to 1 in 5 crashes that happen on the road are fatigue-related.<\/a><\/p>\n\n\n\n

\"Tram
Tram driver fell asleep at the wheel in California<\/figcaption><\/figure>\n\n\n\n

For decades, the transportation industry has tried to solve this problem from the outside in: monitoring lane drift, tracking steering wheel corrections, watching for yawning through a dashboard camera. These approaches are useful, but they share a fundamental flaw. By the time fatigue is visible in a driver’s behavior or vehicle control, it’s already advanced far enough to be dangerous. The warning comes too late.<\/p>\n\n\n\n

The most logical solution, then, is to go directly to the source. Not to watch what fatigue does to a driver’s hands or eyes or lane position, but to monitor what it does to the organ where it originates: the brain.<\/p>\n\n\n\n

What Is Driver Fatigue, And Why Is It So Hard to Detect?<\/strong><\/h2>\n\n\n\n

Most people think of driver fatigue as simply feeling sleepy at the wheel. In reality, it’s a much broader and more complex physiological state,  one that begins long before a driver’s eyes grow heavy, and one that progressively dismantles almost every cognitive function required to drive safely.<\/p>\n\n\n\n

Fatigue isn’t a switch that flips. It’s a spectrum.<\/p>\n\n\n\n

As fatigue deepens, the effects become more pronounced. Reaction times slow measurably. The ability to anticipate hazards, one of the most critical skills in driving, degrades. Decision-making becomes impulsive rather than considered. Peripheral vision narrows. The driver starts missing details in the environment that an alert mind would catch automatically.<\/p>\n\n\n\n

Then come microsleeps.<\/p>\n\n\n\n

These are the moments that turn tired driving into genuinely lethal driving. Microsleeps are involuntary episodes of sleep that last between one and thirty seconds, during which the brain essentially goes offline. The driver’s eyes may remain open. They may be gripping the steering wheel. But they are not processing what is in front of them. At 100 km\/h, a ten-second microsleep means the vehicle travels nearly 280 metres with no one truly in command. On a motorway, that is more than enough to miss a bend, rear-end a slowing vehicle, or drift across a lane divider into oncoming traffic.<\/p>\n\n\n\n

What makes this progression so dangerous is the same thing that makes it so difficult to address: fatigue impairs the brain’s ability to accurately assess its own level of impairment.<\/strong> Study after study has shown that fatigued drivers consistently overestimate their own alertness. They genuinely believe they are capable of continuing safely, right up until the moment they are not. This is fundamentally different from, say, mechanical fatigue in a vehicle component, where wear and degradation can be measured objectively and reliably. Human fatigue resists that kind of straightforward quantification.<\/p>\n\n\n\n

Why Traditional Detection Methods Fall Short<\/strong><\/h3>\n\n\n\n

Given how dangerous driver fatigue is, the transportation industry has invested heavily in tools to detect and manage it. But most of the solutions that have been widely adopted share a critical structural limitation: they are reactive rather than proactive.<\/strong> They wait for fatigue to manifest in observable behaviour before they intervene. And by that point, the risk is already present.<\/p>\n\n\n\n

Camera-based behavioural monitoring<\/strong> is the most common approach. These systems use infrared cameras pointed at the driver’s face to track eye closure duration, blink frequency, yawning patterns, and head position. The underlying logic is sound, these are genuine indicators of fatigue. But they are indicators of advanced<\/em> fatigue. A driver yawning repeatedly or struggling to keep their eyes open has already moved well past the early warning zone. The system is catching the visible symptoms of a problem that started much earlier, neurologically speaking.<\/p>\n\n\n\n

Vehicle-based systems<\/strong> take a different approach, monitoring the car itself rather than the driver, tracking lane departures, erratic steering corrections, unusual braking patterns, and deviations from expected driving behaviour. These systems are even further downstream. By the time fatigue has degraded a driver’s vehicle control enough to trigger a lane-departure alert, their cognitive performance has been compromised for some time. There’s also a significant contextual problem: poor lane keeping can be caused by road conditions, vehicle handling, distraction, or deliberate manoeuvring. Attributing it reliably to fatigue requires a level of context these systems often lack.<\/p>\n\n\n\n

Self-reporting and driving hour regulations<\/strong> are the industry’s oldest tool, relying on legal limits for consecutive driving hours and mandatory rest periods. These are valuable from a policy perspective, but they assume that fatigue correlates predictably with hours driven, which it doesn’t. A driver who slept poorly the night before may be dangerously fatigued two hours into a shift. A driver who is well-rested and at the peak of their circadian rhythm may be perfectly alert after eight hours of driving. Hours-of-service rules set a ceiling; they don’t measure what’s actually happening in a driver’s brain at any given moment.<\/p>\n\n\n\n

Heart rate and HRV monitoring<\/strong> through smartwatches or embedded sensors get closer to the physiological root of the problem. Heart rate variability \u2014 the subtle variation in time between heartbeats \u2014 does change with fatigue and stress, and wearable devices can track this in real time. It’s a meaningful signal. But the correlation between HRV and cognitive drowsiness is indirect. The heart is responding to the same underlying fatigue that affects the brain, but it’s not the primary source of alertness. It’s a downstream indicator, two steps removed from where the problem actually originates.<\/p>\n\n\n\n

The Case for Going Straight to the Source<\/strong><\/h3>\n\n\n\n

If fatigue is, fundamentally, a brain state, a shift in the pattern of neural activity that governs attention, reaction speed, and conscious awareness, then the most direct and earliest way to detect it is to measure the brain itself.<\/p>\n\n\n\n

This is the core logic behind the EEG headband for driver fatigue<\/strong>. Rather than waiting for fatigue to ripple outward into behaviour, vehicle control, or even heart rhythm, EEG captures the change at the neurological level where it begins. The brain starts showing fatigue signatures, specific, measurable shifts in its electrical activity patterns, several minutes before any physical symptom appears. That window of early warning is exactly what every other detection method fails to provide, and it is what makes EEG-based monitoring a genuinely different category of technology, not simply a better version of what already exists.<\/p>\n\n\n\n

The Fatigue Signature: What Drowsiness Looks Like in the Brain<\/strong><\/h3>\n\n\n\n

What makes EEG so powerful for driver fatigue detection isn’t just the ability to see individual frequency bands, it’s the ability to track how they shift relative to each other over time.<\/p>\n\n\n\n

A well-rested, alert driver will show a brain wave profile dominated by beta activity, with moderate, stable levels of alpha and minimal theta. As fatigue begins to set in, a characteristic pattern emerges: beta power decreases, while alpha and theta power increase.<\/strong> The brain is literally transitioning from an active, engaged state toward a passive, drowsy one \u2014 and this transition is visible in the EEG signal several minutes before the driver notices it subjectively, and long before it produces any detectable change in their driving behaviour.<\/p>\n\n\n\n

Researchers and engineers working on fatigue detection systems typically focus on two key metrics derived from this pattern. The first is the theta\/alpha power ratio<\/strong>,  an increase in this ratio is a reliable marker of drowsiness onset. The second is the theta\/beta ratio<\/strong>, as the brain becomes more fatigued, the slower theta frequencies grow stronger relative to the faster, alert beta frequencies, and this ratio rises predictably with fatigue severity.<\/p>\n\n\n\n

These ratios give fatigue detection algorithms something precise and quantifiable to work with: not a subjective impression of whether a driver looks tired, but a mathematical signature drawn directly from neural activity.<\/p>\n\n\n\n

Below is a table that summarizes the efficiency of each method mentioned above. <\/p>\n\n\n\n

Method<\/strong><\/td>Detection Stage<\/strong><\/td>Accuracy<\/strong><\/td>Proactive?<\/strong><\/td><\/tr>
Camera \/ behavioral<\/td>Late (visible symptoms)<\/td>Moderate<\/td>\u274c<\/td><\/tr>
Vehicle-based<\/td>Late (performance drop)<\/td>Low\u2013Moderate<\/td>\u274c<\/td><\/tr>
Heart rate (ECG\/HRV)<\/td>Mid<\/td>Moderate<\/td>Partial<\/td><\/tr>
EEG headband<\/strong><\/td>Early (brain signal)<\/strong><\/td>High<\/strong><\/td>\u2705<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

The Emerging Consensus: Multi-Modal Is the Gold Standard<\/strong><\/h3>\n\n\n\n

The most sophisticated fatigue detection deployments increasingly combine multiple modalities rather than relying on any single approach. An EEG headband for driver fatigue provides the earliest neurological warning. Camera-based behavioural monitoring adds a secondary confirmation layer and catches cases where the headband may be poorly fitted or temporarily removed. Vehicle-based monitoring serves as the final safety net. HRV data from a paired wearable adds context about cumulative fatigue over the shift.<\/p>\n\n\n\n

Each layer catches what the others might miss. The EEG provides the earliest warning; the camera confirms behavioural deterioration; the vehicle system provides a last line of defence. Together, they create a detection architecture that is substantially more robust than any single method alone.<\/p>\n\n\n\n

Who Benefits From EEG Headbands for Driver Fatigue?<\/strong><\/h2>\n\n\n\n

The science of EEG-based fatigue detection is compelling on its own terms. But science becomes meaningful in road safety only when it reaches the people and organisations who need it most. The question of who benefits from an EEG headband for driver fatigue is not a simple one \u2014 the answer spans professional drivers and private commuters, fleet operators and insurance companies, regulators and the families of people who share the road with fatigued drivers every day. Each group experiences the problem differently, and each stands to gain from the solution in distinct ways.<\/p>\n\n\n\n

Long-Haul Truckers and Freight Fleets<\/strong><\/h3>\n\n\n\n

If there is a single category of driver for whom an EEG headband for driver fatigue is most immediately, most urgently relevant, it is the long-haul truck driver.<\/p>\n\n\n\n

The conditions of long-haul trucking create a near-perfect storm of fatigue risk factors. Shifts are long, routes are frequently monotonous, departure times are often dictated by delivery schedules rather than circadian biology. Which means a significant proportion of long-haul driving happens during the 2\u20136 AM window when the human body is neurologically at its lowest ebb regardless of how much sleep the driver has had. And the vehicles involved, articulated lorries, tanker trucks, heavy goods vehicles, carry enough mass and momentum that the consequences of a fatigue-related incident are categorically more severe than a collision involving a passenger car.<\/p>\n\n\n\n

For freight fleet operators, the business case for EEG-based fatigue monitoring is strong and calculable. A single serious accident involving a commercial vehicle generates costs that routinely run into seven figures when vehicle damage, cargo loss, insurance claims, litigation, regulatory penalties, and reputational damage are totalled. The cost of equipping an entire fleet with EEG headbands for driver fatigue is a fraction of that figure. More importantly, it is a proactive cost, one that prevents the incident rather than paying for its aftermath.<\/p>\n\n\n\n

The regulatory environment is also moving decisively in this direction. The European Union’s 2024 mandate requiring fatigue detection technology in all new vehicles sold within member states signals the direction of travel clearly. Fleet operators who invest in EEG-based monitoring now are not just improving their safety standards \u2014 they are positioning themselves ahead of a compliance curve that will eventually become unavoidable.<\/p>\n\n\n\n

\"EEG
Drivers of Di Martino<\/a> wearing the Oraigo’s EEG Headband, Aigo<\/figcaption><\/figure>\n\n\n\n

Bus and Coach Operators<\/strong><\/h3>\n\n\n\n

Passenger transport introduces a dimension that freight operations don’t face in the same way: direct, immediate liability for the safety of people who have no control over the vehicle they are travelling in. When a bus driver is fatigued, the consequences are borne not just by the driver and the operator, but by every passenger on board.<\/p>\n\n\n\n

This creates both a moral and a legal imperative that is particularly acute for bus and coach operators. A fatigue-related accident involving a passenger vehicle generates not just the direct costs of an incident but the reputational damage of a headline that no transport company wants to carry, and the human cost of injuries or fatalities among people who trusted the operator with their safety.<\/p>\n\n\n\n

Bus routes also have specific fatigue risk profiles that EEG monitoring is well positioned to address. Urban bus routes involve intense, cognitively demanding driving, constant stops, pedestrian activity, junction negotiation \u2014 that generates mental fatigue of a different character than motorway monotony but equally dangerous in its effects. Long-distance coach services combine extended driving hours with the circadian vulnerability windows that affect all night-time and early-morning operations. School bus routes, despite their relatively short individual trip duration, involve drivers who often work split shifts with inadequate rest windows between morning and afternoon runs.<\/p>\n\n\n\n

EEG headbands for driver fatigue gives bus operators something they have not previously had: continuous, objective, neurologically grounded data on driver alertness state across the entire operational day \u2014 not just a compliance record showing that hours-of-service rules were technically observed.<\/p>\n\n\n\n

Emergency Services<\/strong><\/h3>\n\n\n\n

Fatigue in emergency service drivers, ambulance crews, fire service vehicles, police, occupies a particularly difficult position. These are the drivers most likely to be operating during the highest-risk hours, under the highest cognitive and emotional load, with the least predictable rest patterns. They are also, by professional culture, among the least likely to self-report fatigue or request relief from duty.<\/p>\n\n\n\n

The shift structures common in emergency services, 12-hour shifts, 24-hour on-call rotations, unpredictable call patterns that fragment sleep, create chronic fatigue accumulation that compounds over days and weeks. A paramedic who has been on call through the night and is responding to a third emergency call at 5 AM is carrying a fatigue burden that no hours-of-service rule captures, because much of the accumulated impairment came from broken sleep rather than from logged driving time.<\/p>\n\n\n\n

EEG monitoring in this context provides something invaluable: an objective, real-time measure of actual neurological state that cuts through both the professional reluctance to admit fatigue and the subjective unreliability of self-assessment. It doesn’t ask the driver how they feel. It measures what their brain is doing.<\/p>\n\n\n\n

There is an important nuance here. Emergency response driving is inherently high-arousal, the adrenaline response to an emergency call produces a temporary surge in alertness that can mask underlying fatigue. EEG monitoring that has established a personalised baseline for a given driver can detect when that arousal-induced alertness is paper-thin, when the driver is running on stress hormones rather than genuine cognitive capacity, and is at elevated risk of rapid deterioration once the adrenaline ebbs.<\/p>\n\n\n\n

Company Car Drivers and Corporate Fleets<\/strong><\/h3>\n\n\n\n

Beyond the heavy commercial vehicle sector, there is a vast and often overlooked population of fatigue-risk drivers: the millions of people who drive company cars as part of their professional role. Sales representatives covering large territories. Field engineers and technicians. Consultants and executives commuting between offices and client sites. Regional managers driving between multiple locations in a single day.<\/p>\n\n\n\n

These drivers rarely think of themselves as professional drivers in the way a truck driver does, and their employers often don’t either. But their exposure to fatigue risk can be substantial. A sales representative who drives 50,000 kilometres a year, frequently on early morning motorway journeys to reach first appointments, is exposed to meaningful fatigue risk that standard fleet management practices, GPS tracking, fuel monitoring, vehicle maintenance, do nothing to address.<\/p>\n\n\n\n

Corporate duty-of-care obligations are also evolving. In many jurisdictions, employers have legal responsibilities for the safety of employees driving on company business that extend beyond simply maintaining the vehicle. The concept of corporate manslaughter has been applied in cases where employers failed to adequately manage foreseeable driver fatigue risk. EEG headbands for driver fatigue, deployed across a corporate fleet, provides both practical protection, reducing actual risk, and documented evidence of proactive risk management that matters significantly in any post-incident legal or regulatory context.<\/p>\n\n\n\n

Individual Commuters and the Consumer Market<\/strong><\/h3>\n\n\n\n

The consumer case for EEG fatigue monitoring is newer and less developed than the fleet case, but it is growing as device costs fall and awareness of fatigue risk increases among private drivers.<\/p>\n\n\n\n

The population most at risk in the consumer segment is identifiable, even if it is not always self-aware. Shift workers, nurses, factory workers, security staff, who drive home after overnight shifts are among the highest-risk drivers on the road at any given moment. Their fatigue is acute, their impairment is neurologically comparable to significant alcohol intoxication, and their journeys home represent a period of intense vulnerability. New parents in the early months of an infant’s life face a similar pattern of sleep deprivation that accumulates dangerously. Long-distance leisure drivers, the family road trip that presses on through the night to reach a destination, make fatigue-related decisions in a context where no regulatory framework applies and no employer has any oversight.<\/p>\n\n\n\n

For all of these drivers, a consumer-accessible EEG headband for driver fatigue offers something that was previously unavailable: a reliable, objective, personal early warning system that doesn’t require them to accurately assess their own alertness, which, as we have established, is precisely the capacity that fatigue impairs most insidiously.<\/p>\n\n\n\n

The market trajectory supports this expansion. As manufacturing costs for dry-electrode EEG technology continue to fall, and as consumer familiarity with health wearables continues to grow, the barrier to adoption of EEG-based fatigue monitoring for private drivers is decreasing steadily. What is a specialist fleet safety tool today is likely to become, within the next decade, a standard feature of personal driving safety equipment.<\/p>\n\n\n\n

Fleet Managers and Safety Officers: The Organisational View<\/strong><\/h3>\n\n\n\n

It would be incomplete to discuss who benefits from EEG headband technology without addressing the organisational layer, the fleet managers, safety officers, and transport directors who are responsible for driver welfare across entire operations rather than individual journeys.<\/p>\n\n\n\n

For these professionals, an EEG headband for driver fatigue is not just a device that protects individual drivers. It is a data infrastructure that transforms how fatigue risk is understood and managed at the organisational level.<\/p>\n\n\n\n

The real-time dashboard data generated by a fleet of EEG-equipped drivers creates visibility that has never previously existed. Not just which driver received a fatigue alert today, but when in their shift the alert occurred, on which route, at what time of day, after how many hours behind the wheel. Aggregated across a fleet and over time, this data reveals patterns, systematic fatigue risk factors embedded in scheduling structures, route profiles, or operational practices, that no amount of subjective driver feedback or hours-of-service compliance monitoring would ever surface.<\/p>\n\n\n\n

A fleet manager who can see that fatigue alerts cluster systematically on a particular overnight route, or during the final two hours of a specific shift pattern, or among drivers returning from a particular depot, has actionable intelligence to address the structural causes of fatigue rather than simply managing its individual instances. That is a qualitative shift in what organisational fatigue management actually means, from reactive incident response to genuine, data-driven prevention.<\/p>\n\n\n\n

The compliance dimension is equally significant. As regulatory requirements around driver fatigue management continue to tighten across the EU and other jurisdictions, the ability to produce documented, timestamped records of continuous fatigue monitoring, demonstrating that the organisation actively tracks and responds to driver alertness, provides a level of due diligence documentation that no previous technology has been able to offer. It is the difference between telling a regulator that you take fatigue seriously and being able to show them, in granular detail, exactly how.<\/p>\n\n\n\n

The Brain Knows First<\/strong><\/h2>\n\n\n\n

There is a simple truth at the centre of everything this article has covered. Fatigue is not a driving problem. It is a brain problem that expresses itself through driving. And for decades, the transportation industry has been trying to solve a brain problem by watching what happens to the vehicle.<\/p>\n\n\n\n

The results speak for themselves. Despite widespread adoption of camera-based monitoring, hours-of-service regulations, lane departure warnings, and driver awareness programmes, fatigue remains one of the leading causes of serious road accidents worldwide. The tools have been improving, but they have been improving within a paradigm that is fundamentally limited, one that waits for the brain’s impairment to become visible before responding to it.<\/p>\n\n\n\n

The EEG headband for driver fatigue represents a departure from that paradigm, not an iteration within it. It doesn’t watch the steering wheel for evidence that the brain has already failed. It doesn’t wait for the eyes to close before sounding an alarm. It reads the brain itself, directly, continuously, in real time, and detects the neurological transition toward dangerous drowsiness several critical minutes before any other signal, internal or external, reflects what is happening. That window of early warning is not a marginal improvement. In road safety, where reaction time is measured in fractions of seconds and the consequences of a lapse are measured in lives, minutes of advance notice is a transformative advantage.<\/p>\n\n\n\n

The Moment We Are In<\/strong><\/h3>\n\n\n\n

The EEG headband for driver fatigue sits at an unusual point in the technology adoption curve, advanced enough that the science is settled and the products are deployable at fleet scale, but early enough that the organisations adopting it now are still operating ahead of the mainstream rather than catching up to it.<\/p>\n\n\n\n

This matters because the advantages of early adoption in safety technology are not just operational, they are cumulative. The fleet that begins collecting EEG fatigue data today is building a dataset that will train better predictive models tomorrow. The safety officer who develops expertise in interpreting EEG fatigue patterns now will have institutional knowledge that competitors are still acquiring years from now. The organisation that establishes a culture of driver wellness grounded in objective neurological monitoring builds driver trust, retention, and engagement outcomes that are impossible to replicate quickly.<\/p>\n\n\n\n

The regulatory window is also finite. The EU mandate is already in place. Performance-based physiological monitoring standards are in development. The transport companies that adopt EEG monitoring voluntarily, ahead of requirement, demonstrate proactive leadership to regulators, insurers, and customers. Those that wait for compliance deadlines will find the landscape has changed around them.<\/p>\n\n\n\n

A Final Word on What This Technology Actually Represents<\/strong><\/h3>\n\n\n\n

It is easy to discuss EEG headbands for driver fatigue in the language of technology specifications, market projections, and regulatory frameworks. These are the right languages for procurement decisions and investment cases. But they can obscure what the technology fundamentally represents.<\/p>\n\n\n\n

Every fatigue-related road accident is, at its core, a failure of detection, a moment where the gap between how impaired a driver was and how impaired they knew themselves to be proved fatal. The driver did not choose to fall asleep at the wheel. They did not knowingly continue driving in a dangerous state. They were betrayed by the same neurological mechanism that makes fatigue so dangerous in the first place: its ability to impair judgement about itself.<\/p>\n\n\n\n

An EEG headband for driver fatigue closes that gap. It provides the objective, external measure of brain state that the fatigued brain cannot provide for itself. It is, in a very real sense, a device that knows what the driver does not yet know, and tells them in time to act on it.<\/p>\n\n\n\n

That is not a marginal product improvement in the driver safety market. It is a fundamentally different kind of protection. And the drivers, the fleet operators, the families, and the other road users who benefit from it represent the only measure of this technology’s success that ultimately matters.<\/p>\n\n\n\n

Interested in seeing how EEG-based fatigue detection works in a real operational environment? <\/strong><\/p>\n\n\n\n

Explore Oraigo’s headband technology <\/a>or book a personalised demo <\/a>to find out how it can transform your fleet’s approach to driver safety.<\/p>\n\n\n\n

\"Aigo:
Oraigo’s EEG Headband for Driver Fatigue: Aigo<\/figcaption><\/figure>\n\n\n\n

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The Invisible Danger Behind the Wheel Every day, millions of drivers get behind the wheel carrying a risk they can’t see, measure, or easily control: fatigue. It doesn’t announce itself the way a mechanical failure does. There’s no warning light, no sudden noise, no obvious moment of breakdown. Instead, it creeps in gradually, a slightly […]<\/p>\n","protected":false},"author":3,"featured_media":4056,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[35],"tags":[],"class_list":["post-4379","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-wearable-safety-tech"],"jetpack_featured_media_url":"https:\/\/oraigo.com\/wp-content\/uploads\/2025\/12\/Driver-wearing-EEG-Fatigue-detection-device-Aigo.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/posts\/4379","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/comments?post=4379"}],"version-history":[{"count":2,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/posts\/4379\/revisions"}],"predecessor-version":[{"id":4383,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/posts\/4379\/revisions\/4383"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/media\/4056"}],"wp:attachment":[{"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/media?parent=4379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/categories?post=4379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/oraigo.com\/en\/wp-json\/wp\/v2\/tags?post=4379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}