Alkawadri Rafeed
Human Brain Mapping Laboratory, Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States.
Yale Human Brain Mapping Program, Yale University, New Haven, CT, United States.
Front Neurosci. 2019 Mar 27;13:191. doi: 10.3389/fnins.2019.00191. eCollection 2019.
The main applications of the Brain-Computer Interface (BCI) have been in the domain of rehabilitation, control of prosthetics, and in neuro-feedback. Only a few clinical applications presently exist for the management of drug-resistant epilepsy. Epilepsy surgery can be a life-changing procedure in the subset of millions of patients who are medically intractable. Recording of seizures and localization of the Seizure Onset Zone (SOZ) in the subgroup of "surgical" patients, who require intracranial-EEG (icEEG) evaluations, remain to date the best available surrogate marker of the epileptogenic tissue. icEEG presents certain risks and challenges making it a frontier that will benefit from optimization. Despite the presentation of several novel biomarkers for the localization of epileptic brain regions (HFOs-spikes vs. Spikes for instance), integration of most in practices is not at the prime time as it requires a degree of knowledge about signal and computation. The clinical care remains inspired by the original practices of recording the seizures and expert visual analysis of rhythms at onset. It is becoming increasingly evident, however, that there is more to infer from the large amount of EEG data sampled at rates in the order of less than 1 ms and collected over several days of invasive EEG recordings than commonly done in practice. This opens the door for interesting areas at the intersection of neuroscience, computation, engineering and clinical care. Brain-Computer interface (BCI) has the potential of enabling the processing of a large amount of data in a short period of time and providing insights that are not possible otherwise by human expert readers. Our practices suggest that implementation of BCI and Real-Time processing of EEG data is possible and suitable for most standard clinical applications, in fact, often the performance is comparable to a highly qualified human readers with the advantage of producing the results in real-time reliably and tirelessly. This is of utmost importance in specific environments such as in the operating room (OR) among other applications. In this review, we will present the readers with potential targets for BCI in caring for patients with surgical epilepsy.
脑机接口（BCI）的主要应用领域一直是康复、假肢控制和神经反馈。目前，在耐药性癫痫的治疗方面仅有少数临床应用。对于数百万药物难治性患者中的一部分而言，癫痫手术可能是改变生活的治疗手段。在需要进行颅内脑电图（icEEG）评估的“手术”患者亚组中，癫痫发作的记录以及癫痫发作起始区（SOZ）的定位，至今仍是致痫组织的最佳可用替代标志物。icEEG存在一定风险和挑战，使其成为一个需要优化的前沿领域。尽管已出现多种用于癫痫脑区定位的新型生物标志物（例如高频振荡 - 棘波与棘波），但在大多数实际应用中，它们的整合尚未成熟，因为这需要一定程度的信号和计算知识。临床护理仍然受记录癫痫发作及对发作时节律进行专家视觉分析的原始方法所启发。然而，越来越明显的是，从以小于1毫秒的速率采样并在数天的侵入性脑电图记录中收集的大量脑电图数据中，可以推断出比实际临床实践中通常所做的更多信息。这为神经科学、计算、工程和临床护理交叉的有趣领域打开了大门。脑机接口（BCI）有潜力在短时间内处理大量数据，并提供人类专家读者无法获得的见解。我们的实践表明，BCI的实施和脑电图数据的实时处理是可行的，并且适用于大多数标准临床应用，事实上，其性能通常与高素质的人类读者相当，具有能够实时、可靠且不知疲倦地产生结果的优势。这在特定环境中，如手术室（OR）等应用中至关重要。在本综述中，我们将向读者介绍BCI在手术性癫痫患者护理中的潜在目标。
