Abstract: A real-time multi-channel EEG signal processor based on an on-line recursive independent component analysis is provided. A whitening unit generates covariance matrix by computing covariance according to a received sampling signal. A covariance matrix generates a whitening matrix by a computation of an inverse square root matrix calculation unit. An ORICA calculation unit computes the sampling signal and the whitening matrix to obtain a post-whitening sampling signal. The post-whitening sampling signal and an unmixing matrix implement an independent component analysis computation to obtain an independent component data. An ORICA training unit implements training of the unmixing matrix according to the independent component data to generate a new unmixing matrix. The ORICA calculation unit may use the new unmixing matrix to implement an independent component analysis computation. Hardware complexity and power consumption can be reduced by sharing registers and arithmetic calculation units.

Abstract: An independent component analysis processor conducts real-time operations of multiple-channel parallel signals. The processor includes an input buffering unit for receiving and storing multiple-channel parallel signals, a mean/covariance unit, a centering unit for removing direct current components in the multiple channels parallel signals, a whitening unit for performing a whitening process, and an ICA training unit and an ICA calculating unit that perform an independent component analysis process, to calculate independent components in the multiple-channel parallel signals and separate artifacts from the signals.

Abstract: A real-time multi-channel automatic eye blink artifact eliminator includes a receiving unit for receiving independent component data; a temporary storage unit for saving pieces of the independent component data to form a data segment; a detection unit for detecting an eye blink artifact in the data segment through a sample entropy algorithm to generate a sample entropy value corresponding to the data segment; and a processing unit for determining whether the data segment contains the eye blink artifact according to the sample entropy value to generate an output result, eliminating the eye blink artifact according to the output result and outputting processed independent component data. The receiving unit continuously receives a next piece of the independent component data and the temporary storage unit discards the oldest one and adds a new one to form a new data segment, thereby continuously performing the eye blink artifact elimination to each data segment.

Abstract: A real-time multi-channel EEG signal processor based on an on-line recursive independent component analysis is provided. A whitening unit generates covariance matrix by computing covariance according to a received sampling signal. A covariance matrix generates a whitening matrix by a computation of an inverse square root matrix calculation unit. An ORICA calculation unit computes the sampling signal and the whitening matrix to obtain a post-whitening sampling signal. The post-whitening sampling signal and an unmixing matrix implement an independent component analysis computation to obtain an independent component data. An ORICA training unit implements training of the unmixing matrix according to the independent component data to generate a new unmixing matrix. The ORICA calculation unit may use the new unmixing matrix to implement an independent component analysis computation. Hardware complexity and power consumption can be reduced by sharing registers and arithmetic calculation units.

Abstract: A real-time signal processing system and method based on multi-channel independent component analysis (ICA). A one-pass recursive ICA processor uses a computation module to perform multi-channel ICA on a set of first data to generate a plurality of second data and third data. A noise removing module uses the computation module to identify noise in the second data and remove the identified noise to generate a plurality of fourth data. A reconstruction module uses the computation module to reconstruct the set of first data based on the fourth data and the third data to generate a plurality of fifth data. The one-pass recursive ICA processor, the noise removing module, the reconstruction module and the computation module are all implemented on a single chip, such that the one-pass recursive ICA processor, the noise removing module and the reconstruction module share the same computation module to save hardware resources.

Abstract: An independent component analysis processor conducts real-time operations of multiple-channel parallel signals. The processor includes an input buffering unit for receiving and storing multiple-channel parallel signals, a mean/covariance unit, a centering unit for removing direct current components in the multiple channels parallel signals, a whitening unit for performing a whitening process, and an ICA training unit and an ICA calculating unit that perform an independent component analysis process, to calculate independent components in the multiple-channel parallel signals and separate artifacts from the signals.