A hybrid brain-computer interface combining the EEG and NIRS

Compared to the conventional brain-computer interface (BCI) system, the hybrid BCI provides a more efficient way for the communication between the brain and the external device. The Electroencephalography (EEG) signal and the change of oxygenation in the brain are two prevailing approaches used in the BCI. However, single physiological signal couldn't provide enough information for a satisfied BCI. This paper proposes a hybrid BCI system based on the combination of the EEG signal and the cerebral blood oxygen changes measured by the near-infrared spectroscopy system (NIRS) to detect the state of motor imagery (MI). The result shows that the average recognition rate can achieve above 75.04% and the highest rate 91.11%, which are higher than when only using EEG or NIRS. It suggests that the proposed hybrid BCI system has a good performance in the combination of these two different signals. Further investigation may help develop better BCIs with high accuracy and significant efficiency. © 2012 IEEE.published_or_final_versio

Motor Imagery Decoding Enhancement Based on Hybrid EEG-fNIRS Signals

This study explores the combination of electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to enhance the decoding performance of motor imagery (MI) tasks for brain-computer interface (BCI). The experiment involved measuring 64 channels of EEG signals and 20 channels of fNIRS signals simultaneously during a task of the left-right hand MI. By combining these two types of signals, the study aimed to understand how feature fusion affected classification accuracy for MI. The EEG signals were filtered into three bands ( θ : 4–7 Hz, α : 8–13 Hz, β : 14–30 Hz), while the fNIRS signals were filtered into 0.02-0.08 Hz to improve signal quality for subsequent analysis. The common spatial patterns (CSP) algorithm was utilized to extract features from both EEG and fNIRS signals. This allowed the researchers to create a fused signal with both EEG and fNIRS features that could then be processed using principal component analysis (PCA). Finally, the processed data was fed into a support vector machine (SVM) classifier, which improved the mean accuracy rate of MI to 92.25%. By comparing the classification accuracies obtained with fused and unfused segments of EEG and fNIRS signals, the study discovered that fusing the signals significantly improved classification accuracy by 5%-10%. Furthermore, analyzing the activated brain regions using fNIRS showed that the auxiliary motor cortex was significantly activated during MI. These results demonstrate that hybrid signals with a fusion strategy can enhance the stability and fault tolerance in BCI systems, making them valuable for practical applications

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback