## 3 Methods
### Extension of Symbolic Aggregate Approximation  
In this paper, the intention of pilots in the cognitive process is analyzed and deduced based on physiological characteristics. Among them, how to accurately and efficiently obtain the characteristic variables in physiological signals is the key to realize the pilot's intention identification. The existing physiological signal feature extraction methods mainly use statistical methods to obtain time-based and spectrum-based features [29]. However, characteristics of high dimensionality and plenty of physiological signals are ignored, which \begin{table}
\begin{tabular}{c c} \hline \hline
**Step NO.** & **Pilot’s Control Process** \\ \hline
1 & Take-off run. \\ \hline
2 & The speed is 55 knots and the front wheels are lifted. \\ \hline
3 & Climbing airspeed (70–80 KIAS). \\ \hline
4 & The height is 300 ft, the throttle is 2000–2100 RPM. \\ \hline
5 & Crosswind turn at 500 ft height. \\ \hline
6 & Downwind turn at 900 ft height. \\ \hline
7 & The altitude is changed to 1000 ft and the throttle is 1800 RPM on the downwind leg to enter the cruise. The cruising airspeed is 90 KIAS. \\ \hline
8 & When the aircraft is tangent to the head of the runway, the throttle is closed to 1500 RPM; it descends to 800 ft and prepares for the third turn with an airspeed of 80 KIAS. \\ \hline
9 & The head of the runway is 45–50 degrees from the right-wing of the aircraft, enter the third turn. \\ \hline
10 & The aircraft recovered from the third turn, throttled back to 1300–1400 RPM, extended the flaps at 10 degrees, and the airspeed is 70 KIAS. \\ \hline
11 & Enter 500–550 ft in the fourth turn and recover from 400–350 ft in the fourth turn. \\ \hline
12 & The throttle is 1200–1400 RPM, the flaps at 20 degrees. \\ \hline
13 & Approach airspeed (65 KIAS). \\ \hline
14 & After closing the throttle, the aircraft maintains a certain attitude, angle of pitch, and speed to taxi a certain distance, and then flattens (push the nose down and the aircraft flattens). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Airfield traffic pattern control program. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of Signal** & **Method** & **Notes** \\ \hline EEG & 0.5 Hz high-pass filter & Frequencies less than 0.5 Hz are filtered. \\  & 100 Hz low-pass filter & Frequencies greater than 100 Hz are filtered. \\  & magnetic induction & \\ \hline EDA & 0.3 Hz high-pass filter & Frequencies less than 0.3 Hz are filtered. \\ \hline RESP & 100 Hz low-pass filter & Frequencies greater than 100 Hz are filtered \\ \hline \hline \end{tabular} Notes: Low-frequency signal is retained and high-frequency signal is cut off in low-pass filter [28]. High-frequency signal is retained and low-frequency signal is cut off in high-pass filter.  
\end{table} 
Table 2: The definitions of parameters for data preprocessing. will affect the performance of the latter identification model and reduce the identification accuracy of the model [30]. Therefore, the extension of symbolic aggregate approximation (E-SAX) method is used in this paper to achieve effective dimensionality reduction of physiological signals and reasonable interpretation of data. E-SAX is an improved feature extraction method on the basis of the SAX algorithm proposed by [PERSON] et al. [31]. This method could not only represent continuous time-series values with discrete and abstract symbols, while considering extreme and outlier values but, at the same time, it avoids shortcomings of the original SAX method. These shortcomings are mainly due to the mean approximation that misses some important values [32]. The realization process could be divided into the following steps:  
(1) The numerical sequence is standardized as shown in Equation (1):  
\[B=(A-\mu)/\sigma \tag{1}\]  
where \(A\) is raw data, \(B\) is normalized values, and \(\mu\) and \(\sigma\) are the mean and standard deviation of the original data, respectively. (2) The normalized sequence is divided into equal-length subsequences by piecewise aggregate approximation (PAA).