A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application

© 2016 IEEE. A brain-computer interface (BCI) system provides a convenient means of communication between the human brain and a computer, which is applied not only to healthy people but also for people that suffer from motor neuron diseases (MNDs). Motor imagery (MI) is one well-known basis for designing Electroencephalography (EEG)-based real-life BCI systems. However, EEG signals are often contaminated with severe noise and various uncertainties, imprecise and incomplete information streams. Therefore, this study proposes spectrum ensemble based on swam-optimized fuzzy integral for integrating decisions from sub-band classifiers that are established by a sub-band common spatial pattern (SBCSP) method. Firstly, the SBCSP effectively extracts features from EEG signals, and thereby the multiple linear discriminant analysis (MLDA) is employed during a MI classification task. Subsequently, particle swarm optimization (PSO) is used to regulate the subject-specific parameters for assigning optimal confidence levels for classifiers used in the fuzzy integral during the fuzzy fusion stage of the proposed system. Moreover, BCI systems usually tend to have complex architectures, be bulky in size, and require time-consuming processing. To overcome this drawback, a wireless and wearable EEG measurement system is investigated in this study. Finally, in our experimental result, the proposed system is found to produce significant improvement in terms of the receiver operating characteristic (ROC) curve. Furthermore, we demonstrate that a robotic arm can be reliably controlled using the proposed BCI system. This paper presents novel insights regarding the possibility of using the proposed MI-based BCI system in real-life applications

Efficient Blind Source Separation Algorithms with Applications in Speech and Biomedical Signal Processing

Blind source separation/extraction (BSS/BSE) is a powerful signal processing method and has been applied extensively in many fields such as biomedical sciences and speech signal processing, to extract a set of unknown input sources from a set of observations. Different algorithms of BSS were proposed in the literature, that need more investigations, related to the extraction approach, computational complexity, convergence speed, type of domain (time or frequency), mixture properties, and extraction performances. This work presents a three new BSS/BSE algorithms based on computing new transformation matrices used to extract the unknown signals. Type of signals considered in this dissertation are speech, Gaussian, and ECG signals. The first algorithm, named as the BSE-parallel linear predictor filter (BSE-PLP), computes a transformation matrix from the the covariance matrix of the whitened data. Then, use the matrix as an input to linear predictor filters whose coefficients being the unknown sources. The algorithm has very fast convergence in two iterations. Simulation results, using speech, Gaussian, and ECG signals, show that the model is capable of extracting the unknown source signals and removing noise when the input signal to noise ratio is varied from -20 dB to 80 dB. The second algorithm, named as the BSE-idempotent transformation matrix (BSE-ITM), computes its transformation matrix in iterative form, with less computational complexity. The proposed method is tested using speech, Gaussian, and ECG signals. Simulation results show that the proposed algorithm significantly separate the source signals with better performance measures as compared with other approaches used in the dissertation. The third algorithm, named null space idempotent transformation matrix (NSITM) has been designed using the principle of null space of the ITM, to separate the unknown sources. Simulation results show that the method is successfully separating speech, Gaussian, and ECG signals from their mixture. The algorithm has been used also to estimate average FECG heart rate. Results indicated considerable improvement in estimating the peaks over other algorithms used in this work

Removing Ballistocardiogram Artifact from EEG using Short-and-Long-Term Linear Predictor.

In this paper a novel source extraction method is proposed for removing Ballistocardiogram (BCG) artifact from EEG. BCG appears in EEG signals recorded simultaneously with fMRI. The proposed method is a semiblind source extraction algorithm based on linear prediction technique. We define a cost function according to a joint short-and-long-term prediction strategy to extract the BCG sources. We call this method SLTPBSE standing for short-and-long-term prediction blind source extraction. The objective of this work is to i) model the temporal structure of the sources using short-term prediction and ii) impose the prior information about the BCG sources using long-term prediction. These two procedures are simultaneously implemented to optimize the system. The performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to remove the BCG artifact while preserving the task-related parts of the signal. The results of SLTP-BSE are compared with those of well known BCG removal techniques confirming the superiority of the proposed method

Removing ballistocardiogram artifact from EEG using short- and long-term linear predictor

In this paper a novel source extraction method is proposed for removing Ballistocardiogram (BCG) artifact from EEG. BCG appears in EEG signals recorded simultaneously with fMRI. The proposed method is a semiblind source extraction algorithm based on linear prediction technique. We define a cost function according to a joint short-and-long-term prediction strategy to extract the BCG sources. We call this method SLTPBSE standing for short-and-long-term prediction blind source extraction. The objective of this work is to i) model the temporal structure of the sources using short-term prediction and ii) impose the prior information about the BCG sources using long-term prediction. These two procedures are simultaneously implemented to optimize the system. The performance of the proposed method is evaluated using both synthetic and real EEG data. The obtained results show that the proposed technique is able to remove the BCG artifact while preserving the task-related parts of the signal. The results of SLTP-BSE are compared with those of well known BCG removal techniques confirming the superiority of the proposed method