Brain Computer Interface Laboratory

Brain Computer Interface Laboratory

Laboratory Head: Dr. Lin Yao

Brain-computer Interface (BCI) provides a non-muscular communication channel from the brain to the external environment bypassing the peripheral nervous and muscle systems. The BCI lab at the Institute of Neurorehabilitation Systems focuses on the generation and decoding of different brain signals associated with motor and sensory intention, augmentation of the BCI system, and its clinical studies in neurorehabilitation for stroke patients.


Laboratory activities


1. MRCP based fast Brain Switch and Closed-loop BCI system for another type of Neuromodulation

Idle state detection is one of the key issues facing BCI community. We have designated an ultra-fast and robust non-invasive brain switch based on the movement-related cortical potential (MRCP) signal modality (0.05~3Hz), which detects user’s motor intention within a few hundred ms and transfer the detected intention into commands to external device, resulting in a real-time closed-loop BCI. Fulfilled the Hebbian principle, this type of BCI system also functions as an advanced neuromodulation methodology for inducing neuroplasticity with high efficiency.


2. Somatosensory Attention Decoding and Enhanced BCI system Augmented with Sensory Stimulation

For the past two decades, the emergence of different BCI modalities has shown to largely increase BCI research and applications. Based on oscillatory dynamics of the somatosensory cortex (8~26Hz), a new type of tactile BCI system based on selective sensation were firstly proposed, which successfully decodes subjective afferent sensation intention, complementary to efferent motor output (motor imagery). With the help of tactile stimulation, we found an interesting phenomenon that combining motor imagery (efferent output) and selective sensation (afferent input) forming a novel hybrid BCI system could greatly increase BCI performance and solve the “BCI-Illiteracy” problem. Moreover, through proprioceptive stimulation (kinesthesia illusion), the performance of the BCI system could be further improved complementary to the advanced machine learning approaches.


3. Sensory-Motor Integration BCI for Cortical Plasticity Induction and Its application for Stroke Recovery

 Upon previous theoretical and experimental studies, sensory-motor integration BCI would have a great interest for inducing cortical plasticity through Operant Conditioning training or Pavlovian Conditioning longer-term paired training. These will be helpful for those strokes with motor or sensory deficient, whilst with participation of the volitional sensory and motor intention, which BCI system tries to decode, would greatly speed up the rehabilitation process.


4. Combining Non-invasive Cortical Stimulation and Novel BCI systems for Enhanced Neurorehabilitation

Non-invasive cortical stimulation (Transcranial Magnetic Stimulation, TMS), has shown great interest not only for the measurement of the cortical plasticity but also another type of neuromodulation for increasing the cortical activation. The combination of the fast brain switch based closed-loop BCI system and targeted central nervous system stimulation has the potential for augmented neurorehabilitation in motor or sensory recovery.


Representative Publications and BCI Awards

1. Yao L., Meng J.J., Zhang D.G., Sheng X.J., Zhu X.Y., “Selective Sensation based Brain-Computer Interface via Mechanical Vibrotactile Stimulation”, Plos ONE, 2013, 8(6):e64784.

2. Yao L., Meng J.J., Sheng X.J., Zhang D.G., Zhu X.Y., “Combining Motor Im- agery with Selective Sensation towards a Hybrid-Modality BCI”, IEEE Transactions on Biomedical Engineering, 2014, 61(8):2304-2312.

3. Yao L., Meng J.J., Sheng X.J., Zhang D.G., Zhu X.Y., “A Novel Calibration and Task Guidance Framework for Motor Imagery BCI via Tendon Vibration In- duced Sensation with Kinesthesia Illusion”, Journal of Neural Engineering, 2015, 12(1):016005.

4. Jiang, N., Gizzi, L., Mrachacz-Kersting, N., Dremstrup, K., & Farina, D. (2015). A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clinical Neurophysiology, 126(1), 154-159.

5. R. Xu, N. Jiang, C. Lin, N. Mrachacz-Kersting, K. Dremstrup, and D. Farina, “Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications.,” IEEE Trans. Biomed. Eng., vol. 61, no. 2, pp. 288–96, Feb. 2014.

6. R. Xu, N. Jiang, N. Mrachacz-Kersting, C. Lin, G. Asín, J. C. Moreno, J. L. Pons, K. Dremstrup, and D. Farina, “A closed-loop brain-computer interface triggering an active ankle-foot orthosis for inducing cortical neural plasticity,” IEEE Trans. Biomed. Eng., vol. 61, no. 7, pp. 2092–2101, 2014.

7. Imran K. Niazi, Ning Jiang, Dario Farina, "Detection of movement intention from single-trial movement-related cortical potentials", Journal of Neural Engineering, vol. 8, No. 6, 066009, Oct. 2011.


BCI Award 2015, 10 nominated projects

N Mrachacz-Kersting1, L Yao2, S Gervasio1, N Jiang3, BD Ebbesen1, TS Palsson1, TG Nielsen1, E Kamavuako1, R. Xu2, D. Falla2, K Dremstrup1, D Farina2 (1Sensory-Motor Interaction, Aalborg University, DK,2 University Medical Center, Göttigen, DE, 3University of Waterloo, CA)
A Brain-Computer-Interface to combat musculoskeletal pain


BCI Award 2014, the 3rd place

N. Mrachacz-Kerstinga, N. Jiangb, S. Aliakbaryhosseinabadia R. Xub, L. Petrinia, R. Lontisa, M. Jochumsena, K. Dremstrupa, D. Farinab (aSensory-Motor Interaction, Department of Health Science and Technology, DK, bDept. Neurorehabilitation Engineering Bernstein Center for Computational Neuroscience University Medical Center, DE)
The changing Brain: Bidirectional learning between algorithm and user


BCI Award 2013, 10 nominated projects

N. Jianga, N. Mrachacz-Kerstingb, R. Xua, K. Dremstrupb and D. Farinaa (aDepartment of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience, University Medical Center, Göttingen, DE, bCenter for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Aalborg, DK)
An Accurate, Versatile, and Robust Brain Switch for Neurorehabilitation


BCI Award 2012, 10 nominated projects:

N. Mrachacz-Kerstinga, N. Jiangb, K. Dremstrupa, D. Farinac
(aCenter for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, DK-9220 Aalborg, Denmark bStrategic Technology Management, Otto Bock HealthCare GmbH, Duderstadt, Germany cNeurorehabilitation Engineering Bernstein Center for Computational Neuroscience University Medical Center, Göttingen, Germany)
A novel Brain-Computer Interface for Chronic Stroke Patients.