Enhanced cochlear implant coding using stochastic beam-forming

人工耳蜗术是通过电刺激耳蜗神经(听力神经)来恢复深度聋人的听力。理论上，CI使用电刺激来激发神经活动(即，耳蜗神经纤维的放电)，这模仿了正常听力中可能发生的神经活动。然而，正常神经活动的一个方面是常规CI刺激不能模仿的，那就是耳蜗神经纤维的粗大随机‘放电’--即使在没有输入刺激的情况下，这种活动也会发生。在正常耳朵中，这种粗略的随机活动是由毛细胞中将声音信号转换为电信号的内部噪声源引起的。噪音有几个来源，例如：毛细胞的纤毛(毛发)的布朗运动，以及将信号从毛细胞传递到耳蜗神经纤维的化学物质的随机释放。这些噪声源在聋人的耳朵中是不存在的，因为深度耳聋与毛细胞的完全丧失有关。然而，现在有相当多的证据表明，这些噪声源是正常神经编码的重要组成部分，因此我们以前曾提出，应该通过将噪声源合并到CIS中，将它们重新引入到聋人的耳朵中。但传统上，噪声被视为一种滋扰，这是有充分理由的；如果以不受控制的方式添加噪声，几乎肯定会导致人工耳蜗植入者的言语理解能力下降。为了有用，刺激特定耳蜗神经纤维的噪声波形必须与刺激邻近纤维的噪声波形不同--这将确保邻近纤维的激发将是独立的。简单地向每个手术植入的电极施加噪声电流不太可能产生所需的独立性；这是因为耳蜗内充满了导电盐溶液，导致来自电极的电流在整个耳蜗区扩散；因此，噪声电流相互作用并导致在广泛的空间范围内强烈相关的有效刺激。为了绕过这个问题，我们开发了一种减少电流扩散影响的技术。每个电极的噪声电流来自独立噪声源的总和，每个噪声源由一个加权项进行缩放；这些权重可以被选择来产生具有指定的去相关长度(刺激变得不相关的距离)的空间随机场。通过这种方式，可以在耳蜗神经纤维群体中实现准独立的放电。我们将这种技术称为随机波束成形，因为它依赖于噪声源的非相干求和来产生零相关的‘波束’--这个概念类似于天线阵列中的波束成形。初步的计算研究表明，这种方法似乎是可行的和非常稳健的。我们建议扩大我们的初步研究，并使用更完整的电刺激耳朵模型。关键的是，我们将在Clarion人工耳蜗术(Advanced Bionics Ltd.)的用户中测试这种方法。我们将衡量我们的策略在多大程度上实现了独立的噪音刺激，并将衡量植入者语音理解能力的改善。这些测试将在伦敦圣托马斯医院进行，并与马里兰大学的莫尼塔·查特吉博士和先进仿生公司合作进行。这项研究的重要性怎么强调都不为过。在我们以前和现在由EPSRC资助的建模工作中，我们已经清楚地展示了使用噪声来提高语音理解的潜力。然而，只有当我们能够在耳蜗神经纤维群体的神经冲动之间获得更大的独立性时，这种方法才能在实践中发挥作用。这项工作是使我们能够实现随机编码策略所承诺的好处的关键步骤。

Cochlear implants (CIs) are used to restore the hearing of profoundly deaf people by electrical stimulation of the cochlear nerve (the nerve of hearing). Notionally, a CI uses electrical stimulation to evoke neural activity (i.e. firing of the cochlear nerve fibres) that mimics the neural activity that would have occurred in normal hearing. However, one aspect of normal neural activity that is not mimicked by conventional CI stimulation is the gross random 'firing' of cochlear nerve fibres - this activity occurs even in the absence of an input stimulus. In the normal ear, this gross random activity is caused by sources of internal noise in the hair cells that transduce sound signals into electrical signals. The noise has several sources such as: Brownian motion of the stereocilia (hairs) of a hair cell, and the random release of the chemicals that transmit the signal from a hair cell to cochlear nerve fibres. These noise sources are absent in the deafened ear because profound deafness is associated with complete loss of the hair cells. There is now considerable evidence, however, that these noise sources are an essential part of normal neural coding and we have therefore previously proposed that they should be re-introduced back into the deafened ear by incorporating noise sources into CIs.But traditionally noise is regarded as a nuisance, and for good reason; if the noise is added in an uncontrolled manner it will almost certainly lead to worse speech comprehension for cochlear implantees. To be useful the noise waveform that excites a particular cochlear nerve fibre must be dissimilar to those that stimulate neighbouring fibres - this will ensure that the firing of adjacent fibres will be independent. Simply applying a noisy current to each surgically implanted electrode is unlikely to produce the desired independence; this is because the cochlea is filled with conductive salt solutions that causes the currents from the electrodes to spread throughout the cochlea; the noise currents therefore interact and result in an effective stimulus that is strongly correlated over a wide spatial range. To circumvent this problem we have developed a technique that reduces the effect of the current spread. The noise currents for each electrode are derived from a sum of independent noise sources, each scaled by a weighting term; these weights can be chosen to produce a spatial random field with a specified de-correlation length (the distance over which the stimulus becomes uncorrelated). In this manner quasi-independent firing can be achieved across a population of cochlear nerve fibres. We refer to this technique as stochastic beamforming because it relies on the incoherent summation of the noise sources to produces 'beams' of zero correlation - this concept is similar to beamforming in antenna arrays. A preliminary computational study has shown that this approach appears feasible and extremely robust.We propose to extend our preliminary study and use more complete models of the electrically stimulated ear. Critically, we will test the approach with users of the Clarion cochlear implant (Advanced Bionics Ltd). We will measure the extent to which our strategy enables independent noise stimulation and we will measure the improvement to the speech comprehension of implantees. These tests will be done at St Thomas' Hospital (London) and in collaboration with Dr Monita Chatterjee (University of Maryland) and Advanced Bionics. The importance of this study cannot be overstated. In our previous and current EPSRC-funded modelling work, we have clearly demonstrated the potential for using noise to improve speech comprehension. The method, however, will only work in practice if we can get greater independence between the nerve impulses for the population of cochlear nerve fibres. This work is the essential step that will enable us to realise the benefits that stochastic coding strategies promise.