A robust beamforming approach for early detection of readiness potential with application to brain-computer interface systems

Abstract

Early detection of intention to move, at self-paced voluntary movements from the activities of neural current sources on the motor cortex, can be an effective approach to brain-computer interface (BCI) systems. Achieving high sensitivity and pre-movement negative latency are important issues for increasing the speed of BCI and other rehabilitation and neurofeedback systems used by disabled and stroke patients and helps enhance their movement abilities. Therefore, developing high-performance extractors or beamformers is a necessary task in this regard. In this paper, for the sake of improving the beamforming performance in well reconstruction of sources of readiness potential, related to hand movement, one kind of surface spatial filter (spherical spline derivative on electrode space) and the linearly constrained minimum variance (LCMV) beamformer are utilized jointly. Moreover, in order to achieve better results, the real head model of each subject was created, using individual head MRI, and was used in beamformer algorithm. Also, few optimizations were done on reconstructed source signal powers to help our template matching classifier with detection of movement onset for five healthy subjects. Our classification results show an average true positive rate (TPR) of 77.1% and 73.1%, false positive rate (FPR) of 28.96% and 28.74% and latency of -512.426 ±396. 7ms and - 360.29 ±252. 16 ms from signals of current sources of motor cortex and sensor space respectively. It can be seen that the proposed method has reliable sensitivity and is faster in prediction of movement onset and more reliable to be used for online BCI in future.

Relaxed Zero-Forcing Beamformer under Temporally-Correlated Interference

“…✷ Proof of Theorem 2: Substituting (37) and (38) into (31) yields (20) and (22). The MSE (21) of ZF is verified by considering the limit of λ ε → +∞ in (14). The rest to prove is (19), in which the cases of γ ≤ 0 and γ ≥ 1 can be verified straightforwardly from Theorem 1.…”

“…The rest to prove is (19), in which the cases of γ ≤ 0 and γ ≥ 1 can be verified straightforwardly from Theorem 1. The case of γ ∈ (0, 1) can be verified by substituting ( 15) -( 18) into (14). Remark 4.…”

“…Focusing on EEG, this is especially true for applications requiring online processing of beamformer output such as brain-computer interface (BCI) [13,14] and source-space EEG neurofeedback, which is currently another hot topic in brain signal processing [15,16]. To date, the dominant beamforming technique used in these fields has been the MVDR beamformer, see, e.g., [17,18,19].…”

Versatile Brain-Computer-Interface for Severely -Disabled People

“…These rely on the weak Electroencephalogram (EEG) signals that arise from the neural activities and are measured using non-invasive electrodes suitably placed on the skull's surface. Introduced by Vidal in 1973 [1], BCI is currently an active research direction with applications in diverse areas including intelligent home control [2], speech synthesis [3], spelling applications [4], readiness detection [5], Epilepsy Prognosis [6], wheelchair control [7], microsleep prevention [8], limb rehabilitation [9], mobile robots [10,11], drowsiness control [12], and assistive systems for people with severe handicaps enabling them, for example, to control electronic devices [13] or browse the internet [14] .…”

Floating Point versus Fixed point Tradeoffs in FPGA Implementations of QR Decomposition Algorithm

“…The advent of new advanced digital communication systems and military radar applications such as adaptive beamforming [1], Space-Time Adaptive Processing, MIMO systems, etc. has imposed complex and computationally demanding algorithms [2].…”