Electrode bridging is a common but easily ignored EEG artifact source. Based on the distinctive statistical characteristics of mutual information, a novel algorithm to automatically detect these bridges was developed and further applied to four EEG data sets acquired from different subjects. The applications identified four, four, three and zero pairs of bridged electrodes in these four data sets, respectively. No influencing factors were returned by One-way robustness analyses across different recording tasks and/or pre-processing procedures. And further comparison experiments performed on simulated data indicated that it outperformed the electrical distance method. All these findings suggest that the novel method is able to screen electrode bridges in a satisfying manner, making it of great significance in providing an indication to timely remedy the contaminated EEG data so as to avoid distortions to the resultant EEG topographies.%电极桥接是一项重要但又极易被忽视的脑电噪声来源.基于互信息的统计特性提出了一种筛查桥接电极的方法，并将该方法应用到了4个被试不同任务下的数据中.4个被试的脑电数据中分别检出3、4、4和0对桥接电极；对采集条件或不同预处理步骤进行的单因素鲁棒性分析未从中发现任何影响因素；进一步的仿真对比实验表明，所提方法较电气距离法更为准确.因此，互信息的统计特性可有效用于检测脑电数据中的电极桥接，进而对及时提醒修复脑电数据或正确解释所分析出的结果具有重要意义.
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