EEG-based classifiers are being used in a growing number of applications, including assisting cognitive function and medical diagnosis. In this brief, a low power EEG signal detection chip is presented based on supporting vector machine (SVM) for classification, with the application example of epilepsy detection. This brief elaborates the detection algorithm and circuit implementation of the neural detection system. The feature extraction module is based on a 256-point FFT for band energies calculation. An exponential operation circuit with scaling unit is proposed to implement the kernel function in classifier. The exponential function operation circuit combines the coordinate rotation digital calculation method (CORDIC) and approximate circuits to obtain power-delay benefit. Fabricated in 130nm CMOS, the proposed chip achieves wide supply range, low power and high rate of correctness. The seizures detection is with an average accuracy of over 80% and sensitivity of 94.4%. The energy consumption of chip is 1.28uJ/class and the area is only 1.8* 1.8mm21.8mm^{2} .

中文翻译：

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基于SVM的低功耗脑电信号分类芯片的实现


基于脑电图的分类器正在越来越多的应用中使用，包括辅助认知功能和医学诊断。本文提出了一种基于支持向量机（SVM）进行分类的低功耗脑电信号检测芯片，并以癫痫检测为例。本文详细阐述了神经检测系统的检测算法和电路实现。特征提取模块基于 256 点 FFT 进行能带能量计算。提出了一种带有缩放单元的指数运算电路来实现分类器中的核函数。指数函数运算电路结合坐标旋转数字计算方法（CORDIC）和近似电路以获得功率延迟效益。该芯片采用 130nm CMOS 制造，具有宽供电范围、低功耗和高正确率。癫痫发作检测的平均准确率超过 80%，灵敏度为 94.4%。芯片能耗为1.28uJ/级，面积仅为1.8*1.8mm21.8mm^{2}。