Variational Bayesian least squares: an application to brain-machine interface data.
Variational Bayesian least squares: an application to brain-machine interface data.
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变分贝叶斯最小二乘法：脑机接口数据的应用。
DOI：
10.1016/j.neunet.2008.06.012
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Schaal,Stefan
中科院分区：
文献类型：
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作者：
Ting,Jo-Anne;D'Souza,Aaron;Yamamoto,Kenji;Yoshioka,Toshinori;Hoffman,Donna;Kakei,Shinji;Sergio,Lauren;Kalaska,John;Kawato,Mitsuo;Strick,Peter;Schaal,Stefan
越来越多的神经科学项目需要对高维数据进行统计分析，例如，根据脑机接口中的大脑记录预测神经放电的行为或人工设备的操作。虽然经典的线性分析技术很流行，但由于不相关、冗余和噪声信息，在高维数据中往往是脆弱的。我们开发了一种稳健的贝叶斯线性回归算法，它可以自动检测相关特征并排除不相关的特征，所有这些都是以计算高效的方式进行的。与标准线性方法相比，新的贝叶斯方法具有正则化、抗过拟合性、计算效率高(与以前提出的变分线性回归方法不同，它适用于具有大量样本和非常高的输入维数的数据集)，并且易于使用，从而显示了其作为其他线性回归技术的替代技术的潜力。我们在合成数据集和几个神经生理学数据集上对我们的技术进行了评估。对于这些神经生理数据集，我们解决的问题是，从猴子手臂运动中收集的肌电数据是否可以从运动皮质的神经活动中忠实地重建出来。结果表明，与文献中的其他方法相比，我们新开发的方法是成功的，并且从神经生理学的角度，证实了最近关于运动皮质组织的发现。最后，我们的算法的一个增量的实时版本证明了我们的方法对于大脑和机器之间的实时接口的适用性。
An increasing number of projects in neuroscience require statistical analysis of high-dimensional data, as, for instance, in the prediction of behavior from neural firing or in the operation of artificial devices from brain recordings in brain–machine interfaces. Although prevalent, classical linear analysis techniques are often numerically fragile in high dimensions due to irrelevant, redundant, and noisy information. We developed a robust Bayesian linear regression algorithm that automatically detects relevant features and excludes irrelevant ones, all in a computationally efficient manner. In comparison with standard linear methods, the new Bayesian method regularizes against overfitting, is computationally efficient (unlike previously proposed variational linear regression methods, is suitable for data sets with large numbers of samples and a very high number of input dimensions) and is easy to use, thus demonstrating its potential as a drop-in replacement for other linear regression techniques. We evaluate our technique on synthetic data sets and on several neurophysiological data sets. For these neurophysiological data sets we address the question of whether EMG data collected from arm movements of monkeys can be faithfully reconstructed from neural activity in motor cortices. Results demonstrate the success of our newly developed method, in comparison with other approaches in the literature, and, from the neurophysiological point of view, confirms recent findings on the organization of the motor cortex. Finally, an incremental, real-time version of our algorithm demonstrates the suitability of our approach for real-time interfaces between brains and machines.