Our research focus over these years has been in applied biomedical signal processing. In recent years, we have been researching in developing cost effective, high quality and user-friendly BCI based assistive systems / devices that could eventually be used to help the disabled as well as the elderly people to lead an independent life. It helps disabled individuals for communication, controlling assistive devices (ADs), selecting letters from a virtual keyboard, operating prosthetic devices, etc., only by using their brain signals. Towards these objectives, many collaborative (with Danish hospitals, industries and other departments at DTU) research works have been initiated and are underway.

SmartRehab: Design and Development of Brain-Controlled Smart and Portable Rehabilitation Kit for Home-Based Stroke Recovery:

Time Duration: 2022-2026
Partner Institutes: Stanford University (USA), DTU Health Tech (Denmark) and Aarhus University (Denmark).
Funded By: Novo Nordisk Foundation, Denmark

In SmartRehab, a pilot study has been presented to develop a portable and wireless ‘BCI (Brain-Computer Interface) Controlled Home-based Rehabilitation Kit’ for upper limb rehabilitation of chronic phase stroke patients. The developed system will be mainly based on motor imagery and functional electrical stimulation (FES) based rehabilitation approaches. BCI is an emerging interdisciplinary field at the intersection of neuroscience and engineering that have the potential to improve the quality of life for individuals with post-stroke paralysis. In paralysis, stroke patients are unable to get signals from their motor cortex (part of the brain that controls movement) to their muscles. Hence, to restore their movements, the use of electrical stimulation is among the effective rehabilitation methods. In conventional therapy, the electrical stimulation is delivered manually by the therapist that limits the active participation of patient’s brain. Therefore, motor imagery based BCI systems are highly preferred, as they use patient’s brain signals to control the stimulation device, which allows a very effective training and promotes neural plasticity (recovery of brain network)

However, the current BCI based stroke rehabilitation systems are available only for clinical settings, and no existing system is completely portable and wireless. Hence, these limitations are overcome in the proposed system. The project is inter/multidisciplinary and includes the development of brain signal processing algorithms, hardware prototype of electrical muscle stimulator, and software module along with the clinical pilot testing of the designed system on stroke patients. The project will have a significant societal impact as it provides a novel solution for health challenges associated with neural disability. Also, it is in line with the EU strategy for the sustainable development goals of “Good Health and Well-Being”, and thus contributes to the EU’s scientific excellence.

Myo-FES: Development of Myo-Controlled FES Muscle Stimulator Device for Upper Limb Stroke Rehabilitation:

In this work, a pilot study has been conducted to develop a compact, portable, and wireless Myo-Controlled FES device (Myo-FES) with embedded flex sensor for upper limb (wrist extension) stroke rehabilitation. The benefits of this type of therapy reside in the brain plasticity mechanism: It is well established that the human brain can in fact "reprogram" itself, changing and arranging neural pathways. Studies have shown that, among other post-stroke rehabilitation therapies, electromyography (EMG)-controlled FES improves motor functions of stroke patients affected by hemiparetic arm and hand. The Myo-FES mainly includes two unique features: 

  • Generation of FES pulse of variable amplitude that is dependent on the potential magnitude of EMG (Myo) signals. The use of EMG feedback allows to analyze the real-time muscle activity and adjusts the electrical stimulation according to the muscle requirement while performing the required action.
  • Interfacing of strain flex sensors, which will provide continuous monitoring of wrist extension and can evaluate the rehabilitation efficiency.

Electromyography and Inertial Motion Sensors Based Wearable
Data Acquisition System for Stroke Patients:

Development of wearable data acquisition systems with applications to human-machine interaction (HMI) is of great interest to assist stroke patients or people with motor disabilities. This project proposes a hybrid wireless data acquisition system, which combines surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. It is designed to interface wrist extension with external devices, which allows the user to operate devices with hand orientations. A pilot study of the system performed on four healthy subjects has successfully produced two different control signals corresponding to wrist extensions. Preliminary results show a high correlation between sEMG and IMU signals, thus proving the feasibility of such a system. The generated control signals can be used to perform real-time control of different devices in daily-life activities, such as turning ON/OFF of lights in a smart home, controlling an electric wheelchair, and other assistive devices. Such a system will help decrease the dependency of disabled people on their caretakers and empower them to perform their daily-life activities independently.

Brainy Home: A Smart Home and Wheelchair Control Application

Powered by Brain Computer Interface:

Smart home applications have become popular, in recent years, to improve the life quality of people, especially for those with motor disabilities. While the smart home applications are controlled with interaction tools such as mobile phone, voice control etc., these may not be appropriate for people with severe health issues that impacts their motor functions, for instance amyotrophic lateral sclerosis (ALS), cerebral palsy, stroke, locked-in-syndrome (LIS) etc. In this research, we have developed a smart home and wheelchair control application in a virtual environment, which is controlled solely by the steady state visual evoked potential (SSVEP) based brain computer interface (BCI) system.

BCI Spellers:

BCI spelling systems are one of the first BCI applications for individuals with lost normal communication abilities. A faster BCI spelling system with dictionary support, named as the DTU BCI Speller, which functions without any direct physical connection (eg, no mouse / keyboard) was developed in one of our earlier collaborative (DTU Elektro & Rigshospitalet) works. Once commercialized, this patented (No. WO2014207008) work, which requires only the user's visual attention, which will be a game shifter for the affected and thus, a great relief for their family. 

An attempt to extend BCI speller to severely disabled individuals such as late stage ALS, a hybrid BCI spelling system combining both the gaze (DTU Management) and BCI technologies was developed. A user would only list to auditory stimuli and make simple eye movements. Successfully this recent work has great future as it allows people to communicate their intentions faster and efficiently.

Another exciting work was the development of an augmentative and alternative communication system for subjects affected by the Wolf-Hirschhorn syndrome. Their communication ability mainly relies on the assistance of a helper, who interprets the motions of their hand on a message board. This work is a step forward towards the development of an electronic aid that can replace the helper. This approach opened up an exciting path to help such disabled individuals to communicate.


BCI for Locomotion:

Wheelchairs are the most commonly used ADs by disabled individuals for locomotion. An attempt to help people with motor disabilities, we have developed a BCI controlled wheelchair navigation system. Simply by looking at a computer screen, the system was able to control a LEGO Mindstorm robot (wheelchair model) in 4 directions; left, right, forward and backward. The success, impact and attractiveness of this system was clearly reflected in its Danish media coverages (in MedicoTeknik, Ingeniøren, Videnskab, ALTOMTEKNIK, Medico Nyt) as well as high impact publications.    

BCI for Drone Control:

Another exciting work with DTU space was a thought controlled flying robot (Drone) to aid locked-in individuals to explore their surroundings using a video camera mounted on the drone. This stimulating work has received great attention and is being continued in our laboratory.

BCI for Neurorehabilitation:

To exploit BCI's attention enhancing ability, a novel BCI system embedded within an immersive 3D virtual reality classroom was developed for neurorehabilitation of ADHD children. The system was concealed as a game with an underlying story to motivate the subjects and they could play the game simply by looking at the computer monitor. This work has received international attention, with a company from the USA (Aware Technologies) having expressed interest in this technology to use in authentic schools, learning centers and eventually in home environments.


BCI for Stroke Rehabilitation:


As an effort to help stroke survivors to improve their motor skills, an inexpensive rehabilitation training system is envisioned (DTU Electrical, Glostrup Hospital & Cachet). Contributions from many students to the advancement of this work have resulted in several notable publications. In recent work with DTU Management and Rigshospitalet, it has been shown that individuals could play a computer game by just thinking about moving their hands and feet. The potential impact of this for rehabilitation is very stimulating, as our results could ultimately promote rehabilitation, which is more efficient, which would result in a better recovery for millions of patients.

Neuro-Stimulation for Treatment of Depression:


An interesting collaborative (PlatoScience) work, we are aiming to develop a system (headset) capable of increasing an individual's brain activity through electrical stimulation for the treatment of depression. It is envisioned that this new product will offer a simple and effective drug-free solution, with no side effects and no technical expertise. 


Ensemble Deep Learning for AFIB Detection:


A significant proportion of worldwide mortality caused by cardiac diseases and atrial fibrillation (AFIB) is one of the most common cardiac arrhythmias among elderly population. Physicians diagnosis of AFIB through visual examination of the electrocardiogram (ECG) recordings. In our research, a novel algorithm to detect short episodes of AFIB using Machine / Deep Learning and Signal Processing methods has been developed which paves the way to extend real-time AFIB detection applications. This research (REAFEL - REAching the Frail ELderly patient for optimizing diagnosis of atrial fibrillation) is supported by the Innovation Fund Denmark (IFD Project No: 6153-00009B).




Deep Learning for Outcome Prediction in the Intensive Care Unit (ICU):

The sensors used to check the vital conditions of patients in the ICU produce huge amounts of data that can be exploited to predict the outcomes about their future conditions. In this collaborative work with Rigshospitalet, we have developed a deep learning model for the mortality prediction using the data from more than 9000 patients in different hospitals of the Capitol Region of Denmark from 2009 to 2016. Both signal processing and statistical methods are used to extract information the first 24 hours of the ICU data to build the model.