Abstract-This paper describes an area and power-efficient VLSI approach for implementing the discrete wavelet transform on streaming multielectrode neurophysiological data in real time. The VLSI implementation is based on the lifting scheme for wavelet computation using the symmlet4 basis with quantized coefficients and integer fixed-point data precision to minimize hardware demands. The proposed design is driven by the need to compress neural signals recorded with high-density microelectrode arrays implanted in the cortex prior to data telemetry. Our results indicate that signal integrity is not compromised by quantization down to 5-bit filter coefficient and 10-bit data precision at intermediate stages. Furthermore, results from analog simulation and modeling show that a hardware-minimized computational core executing filter steps sequentially is advantageous over the pipeline approach commonly used in DWT implementations. The design is compared to that of a B-spline approach that minimizes the number of multipliers at the expense of increasing the number of adders. The performance demonstrates that in vivo real-time DWT computation is feasible prior to data telemetry, permitting large savings in bandwidth requirements and communication costs given the severe limitations on size, energy consumption and power dissipation of an implantable device.Index Terms-B-spline, brain machine interface, lifting, microelectrode arrays, neural signal processing, neuroprosthetic devices, wavelet transform.

Modern microelectrode arrays acquire neural signals from hundreds of neurons in parallel that are subsequently processed for spike sorting. It is important to identify, extract, and transmit appropriate features that allow accurate spike sorting while using minimum computational resources. This paper describes a new set of spike sorting features, explicitly framed to be computationally efficient and shown to outperform principal component analysis (PCA)-based spike sorting. A hardware friendly architecture, feasible for implantation, is also presented for detecting neural spikes and extracting features to be transmitted for off chip spike classification. The proposed feature set does not require any off-chip training, and requires about 5% of computations as compared to the PCA-based features for the same classification accuracy, tested for spike trains with a broad range of signal-to-noise ratio. Our simulations show a reduction of required bandwidth to about 2% of original data rate, with an average classification accuracy of greater than 94% at a typical signal to noise ratio of 5 dB.

Time-frequency domain signal processing of neural recordings, from high-density microelectrode arrays implanted in the cortex, is highly desired to ease the bandwidth bottleneck associated with data transfer to extra-cranial processing units. Because of its energy compactness features, discrete wavelet transform (DWT) has been shown to provide efficient data compression for neural records without compromising the information content. This paper describes an area-power minimized hardware implementation of the lifting scheme for multilevel, multichannel DWT with quantized filter coefficients and integer computation. Performance tradeoffs and key design decisions for implantable neuroprosthetics are presented. A 32-channel 4-level version of the circuit has been custom designed in 0.18-mum CMOS and occupies only 0.22 mm(2) area and consumes 76 muW of power, making it highly suitable for implantable neural interface applications requiring wireless data transfer.

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To make a communication bridge, a highly accurate, cost effective and an independent glove was designed for deaf/mute people to enable them to communicate. The glove translates the sign language gestures into speech according to the American Sign Language Standard. The glove contained flex and contact sensors to detect the movements of the fingers and bending of the palm. In addition an accelerometer was built in the glove to measure the acceleration produced by the changing positions of the hand. Principal Component Analysis (PCA) was used to train the glove into recognizing various gestures, and later classify the gestures into alphabets in real time. The glove then established a Bluetooth link with an Android phone, which was used to display the received letters and words and convert the text into speech. The glove was found to have an accuracy of 92%.

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