Kinect Brain Computer Interface: can we detect neurological damages easier and better?

People who once struggled through stroke are persistently chased by a burning question: shall it ever happen again? How can I avoid it?

Obviously there are some worrying signals of potentially revocable brain damages such as increased blood pressure, sudden nausea or dizziness one needs to watch, but the accuracy of stroke predictions based on occasional body symptoms i.e. external to the brain is rather dubious. All the above mentioned signs can be attributed to disorders of a completely different nature.

Unfortunately, today’s medicine is not able to precisely predict the possibility of a repeated cerebrovascular accident. At best, neurologists can capture its first manifestations based on cognitive or motor disabilities with the help of Computer Tomography (CT), Electroencephalography (EEG) or Magnetoencephalography (MEG). All those methods have their limitations. For instance, the CT X-ray radiation – as many other invasive interventions – can ultimately be damaging for delicate cell structures inside the brain. Ionizing parcels of hard electromagnetic radiation can bump with the cell DNA, causing damage that may lead to cancer. EEG registers mainly the neuron activity on the surface of the cerebral cortex, giving little information about subcortical neuron activity so that clinicians have to extrapolate the picture. MEG is capable of registering signals produced by the currents excited in subcortical areas, thus providing 3d information, but this method is so far quite cumbersome requiring a lot of space and extremely expensive, so not every hospital can afford the MEG equipment.

Could the future technologies offer patients and physicians a more affordable and accurate way to predict the threat of a repeatable stroke early enough to avoid it? I think now we can observe the first signs of this prevention.



The group of BCI researchers from Colombia, Jhon Edison Muñoz Cardona, have designed a novel Brain Computer Interface (BCI) or rather a Brain Kinect Interface (BKI) system which combines biomechanical signals coming from Microsoft Kinect sensors, brainwave signals acquired from Emotiv EPOC EEG [1] and the so called Steady State Visually Evoked Potentials (SSVEP) signals that are our natural responses to visual stimulation at specific frequencies [2].

While a patient is immersed in a rehabilitation game with a predetermined visual stimulus, e.g., trying to manipulate objects with hands motions or eyesight, Brain Kinect Interface is tracking the dependencies (correlations) between the visual, cognitive and motor signals generated by a patient.

Every stimulus is associated with a command that controls a specific action inside the video game. By registering and analyzing data reflecting motions together with visual reaction in combination with EEG signals, the therapist can get a better understanding of what areas of the brain are exactly responsible for a certain motional or visual stimulus and how they are affected by the game. For example, if a patient has certain difficulties in raising an arm due to the acquired paresis, which part of the brain has been damaged by the stroke and is now responsible for the disorder, and is there any progress in the course of rehabilitation therapy?

Brain Interface


Who knows, maybe due to more and more precise mapping between bodily functions and brain topology in the not so distant future one can even address an inverse problem: by tracking external motional manifestations to reconstruct the activities deeply hidden in the brain. Changes in motion patterns would exactly indicate the brain damage within certain areas.

Of course, the ambitious attempt to diagnose brain failures with non-invasive methods using standard, almost consumer-level technologies will take time and require the development of a new generation of highly sensitive, accurate and miniature sensors vs. expensive and bulky contemporary MEG systems.

But there is a noticeable progress in this direction as well.

One of the examples is a miniature atom-based magnetic sensor developed by the National Institute of Standards and Technology (NIST) that was successfully tested already in 2012 as an instrument to measure human brain activity. Experiments verify the sensor’s potential for biomedical applications such as studying mental processes and advancing the understanding of neurological diseases [3].NIST and German scientists used the NIST sensor to measure alpha waves (the deep relaxation wave (7.5-14Hz))in the brain that arise, e.g., when a person is opening and closing her/his eyes.


Signals resulting from stimulation of the patient’s hand were also explored. The measurements were verified by comparing them with the signals recorded by SQUID systems (superconducting quantum interference device) SQUID, the world’s most sensitive commercially available magnetometers that are considered the “gold standard” for such experiments. The NIST mini-sensor is slightly less sensitive than SQUID as yet, but has the potential for a comparable performance while promising advantages in size, portability and cost. Many other similar experiments are on the way.

Would it be possible to use exergames with personalized exercises to diagnose, treat and ultimately cure patients with the conditions caused by the stroke (e.g., hemiparesis), brain trauma, Parkinson’s disease, sclerosis and other neuropathies?

15 million people worldwide who suffer a stroke each year are looking today towards the upcoming new technologies and medical studies with hope and expectations mixed with anxiety [5].



  1. BKI: Brain Kinect Interface, a new hybrid BCI for rehabilitation .J. Muñoz, O. Henao, J. F. López, J. F. Villada. Games for Health Proceedings of the 3rd European conference on gaming and playful interaction in healthcare.
  2. Steady state visually evoked potentials Wikipedia
  3. Human Computer Interaction Group
  4. NIST Mini-sensor Measures Magnetic Activity in Human Brain
  5. World Stroke Organization











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