通过 µECoG 阵列从自由行为大鼠的 S1 皮层预测振动触觉 "是/否 "检测任务中的杠杆按压。

Deniz Kılınç Bülbül, Burak Güçlü
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引用次数: 0

摘要

研究目的脑机接口(BCI)可帮助严重神经功能障碍患者与外界交流。材料与方法:在两只大鼠的S1上植入了16通道铂-铱微电极阵列,并在振动触觉 "是"/"否 "检测任务中记录了μECoG。在这项任务中,训练大鼠在检测到振动触觉刺激时按下右侧杠杆,而在没有检测到振动触觉刺激时按下左侧杠杆。多通道 µECoG 数据通过时频方法进行离线分析,其特征用于对每次试验中的杠杆按压进行二元分类。对几种机器学习算法进行了测试:两只大鼠的心理物理灵敏度(A')相似且较低(0.58)。与大鼠 1(B'':- 0.01)相比,大鼠 2(B'':-0.11)对右杠杆的偏差更大。所有测试算法对杠杆按压的预测准确率均超过 66%,其中支持向量机的平均准确率最高(78%):结论:根据最近的研究,感官反馈会增加 BCIs 的益处。目前的概念验证研究表明,杠杆按压可从 S1 解码;因此,该区域将来可用于双向 BCI。
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Predicting lever press in a vibrotactile yes/no detection task from S1 cortex of freely behaving rats by µECoG arrays.

Aim of the study: Brain-computer interfaces (BCIs) may help patients with severe neurological deficits communicate with the external world. Based on microelectrocorticography (µECoG) data recorded from the primary somatosensory cortex (S1) of unrestrained behaving rats, this study attempts to decode lever presses in a psychophysical detection task by using machine learning algorithms.

Materials and methods: 16-channel Pt-Ir microelectrode arrays were implanted on the S1 of two rats, and µECoG was recorded during a vibrotactile yes/no detection task. For this task, the rats were trained to press the right lever when they detected the vibrotactile stimulus and the left lever when they did not. The multichannel µECoG data was analysed offline by time-frequency methods and its features were used for binary classification of the lever press at each trial. Several machine learning algorithms were tested as such.

Results: The psychophysical sensitivities (A') were similar and low for both rats (0.58). Rat 2 (B'': -0.11) had higher bias for the right lever than Rat 1 (B'': - 0.01). The lever presses could be predicted with accuracies over 66% with all the tested algorithms, and the highest average accuracy (78%) was with the support vector machine.

Conclusion: According to the recent studies, sensory feedback increases the benefit of the BCIs. The current proof-of-concept study shows that lever presses can be decoded from the S1; therefore, this area may be utilised for a bidirectional BCI in the future.

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