Predicting numerical data entry errors by classifying EEG signals with linear discriminant analysis

Daily numerical data entry is subject to human errors, and errors in numerical data can cause serious losses in health care, safety and finance. Difficulty in detecting errors by human operators in numerical data entry necessitates an early error detection/prediction mechanism to proactively prevent severe accidents. To explore the possibility of using multi-channel electroencephalography (EEG) collected before movements/reactions to detect/predict human errors, linear discriminant analysis (LDA) classifier was utilised to predict numerical typing errors before their occurrence in numerical typing. Single trial EEG data were collected from seven participants during numerical hear-and-type tasks and three temporal features were extracted from six EEG sites in a 150-ms time window. The sensitivity of LDA classifier was revealed by adjusting the critical ratio of two Mahalanobis distances as a classification criterion. On average, the LDA classifier was able to detect 74.34% of numerical typing errors in advance with only 34.46% false alarms, resulting in a sensitivity of 1.05. A cost analysis also showed that using the LDA classifier would be beneficial as long as the penalty is at least 15 times the cost of inspection when the error rate is 5%. LDA demonstrated its realistic potential in detecting/predicting relatively few errors in numerical data without heavy pre-processing. This is one step towards predicting and preventing human errors in perceptual-motor tasks before their occurrence.