Cognitive requirements known to impact activities of daily living in both healthy individuals and those with motor impairments have been difficult to measure due to methodological limitations. Currently, subjective assessments and EEG are the closest tools available for direct or indirect measurement of the internal feeling of naturalness and ease of using an assistive device such as a prosthesis. EEG provides a possible method to directly measure the ease of completing a task with high temporal resolution. This study uses EEG to measure the cognitive load of three tasks: sitting, standing, and walking. The P3 response found in this study was lowest during walking, indicating that walking was the most cognitively burdensome task. These results support those of prior studies including those that have compared the P3 responses during walking and sitting2,14,17,34,35.
Dual task methods have shown that balance control is affected by divided attention in sitting, standing, and walking in individuals with motor impairments14,36 and without motor impairments37,38. EEG studies have also shown decreases in cognitive load as task complexity increases during sitting39, across tasks of standing and walking18,40,41, and across tasks of sitting, standing and walking17. In EEG during walking, cortical fluctuations have been shown to be coupled with phases of the gait cycle42, but understanding the cognitive requirements of various motor tasks remains elusive. One study that measured the impact of supraspinal input on walking found that obstacle avoidance could be seen from spectral fluctuations in EEG signal43.
In agreement with the results of Protzak et al.17, our results indicate that walking had the lowest P3 amplitude (Figure 2); however, we showed similar P3 amplitude for both the sitting and standing tasks. This finding was in contrast to at least one other study which compared the cognitive load of sitting and standing: a dual-task study that found slower reaction times during standing compared to sitting38. While not significantly different, Protzak et al. also showed higher cognitive load for standing compared to sitting17. Furthermore, the visual task used by Protzak et al.17 was different from that of the current study, in which we used an auditory oddball task since it is easier to administer auditory stimuli compared to visual stimuli in unconstrained environments.
A limitation of this study is that the auditory task did not distinguish between the cognitive load between sitting and standing. Tasks that are nearly equally easy, as in the case of sitting and standing in able-bodied individuals, are not expected to yield differences in P3 unless the cognitive task is difficult enough. In contrast to the auditory task used in the current study, the visual task used by Protzak et al.17was able to distinguish the cognitive load for standing compared to sitting in younger participants. This may have been due to the fact that visual tasks are more difficult to complete during activities that require trunk support, as the balance required to maintain posture relies heavily on the visual system. Thus the auditory oddball task used here may have been too easy to distinguish between cognitive load of sitting and standing Figure 3. (a) Histogram of accelerometer RMS magnitude for each condition (sit, stand, and walk). (b) Mean P3 voltage at Pz plotted against the associated motion RMS vector for each subject in each session and condition. Axes show the Gaussian probability distributions for each condition. (c) Averaged ERP for 8 levels of head motion during walking. (d) Mean P3 voltage at Pz from ERP data presented in (c), plotted against motion level, with each head motion level represented by a different color.
Table 1
Estimated gaussian parameters for RMS head motion and average P3 for each condition, computed from averaged subject data shown in Figure 3b.
Condition | RMS Head Motion | Average P3 (µV) |
| mean (st. dev.) | mean (st. dev.) |
Sit | .0085 (.002) | 6.651 (4.58) |
Stand | .0111 (.004) | 6.733 (5.54) |
Walk | .1209 (.022) | 2.809 (8.39) |
for the able-bodied individuals who participated in this study. The advantage of using auditory tasks is that they only require headphones, as compared to visual tasks which require an environment outfitted with LEDs such as those used in the aforementioned study17. Future work may consider the use of a more difficult auditory task, or a visual task in augmented reality to maintain the possibility of administering them in unconstrained, outdoor environments.
While the auditory oddball task used in this study is appropriate for distinguishing cognitive requirements of sitting compared to walking, it might be too simple to cause a change in the cognitive response shown in the ERP in populations without motor impairments when comparing sitting to standing. However, the lack of a difference in P3 between sitting and standing is an interesting finding that could inform future work with different tasks and populations with motor impairments. We expect, for example, that individuals with poorer trunk support and balance would not find sitting and standing to be equally easy and thus would have lower P3 amplitude for standing compared to sitting. Another area of interest is for lower-limb prosthesis users. It is possible to use this paradigm to evaluate changes in cognitive load for users of different devices. For example, a microprocessor knee that provides stance support may be easier for a person to use while standing compared to a purely mechanical device that does not provide stance control.
We also investigated the possible impact of motion artifacts on the walking trials, which has the most head movement of all the tasks. We did not find any significant results indicating that motion artifacts affected the interpretation of the results. A significant trend in Figure 3c would indicate there is an increase in voltage as a function of motion, which would mask a possible decrease in P3 potential during a more difficult dual-task condition. This could lead to the erroneous result that cognitive load is not as high for activities that increase head motion, such as walking and jogging compared to sitting and standing.
This study utilized artifact removal processes including independent components analysis (ICA) to separate out the
contribution of motion (among others, such as eye-blink, muscular, and cardiovascular artifacts). Trials with artifacts were discarded, resulting in a sufficient number of trials (37 per subject in each condition). Despite the availability and usage of ICA methods, dry EEG headsets have not been widely used in dynamic environments due to poor signal quality, which may vary greatly across dry EEG systems29,30. Oliveira et al.44 reported that no data was usable (i.e., 100% of epochs were discarded) using their dry EEG system after excluding epochs that exceeded a threshold difference of 75 µV from baseline. In contrast, our results yielded a suitable amount of clean epochs from EEG recorded during walking using the same standard threshold as in Oliveira et al.44 While there is proprietary information that may help explain the differences in dry EEG technology, the system used in the current study may provide superior signal quality in part due to a stabilization strap, spring-loaded electrodes, and a common mode follower. The common mode follower measures the external electrical activity from the environment so that it can be removed from the EEG signal.
This study provides a method for measuring cognitive load using a dry EEG interface that is robust to tasks of various dynamic movement artifact. Current methods in EEG allowed the measurement of EEG during mobile activities in ecologically relevant settings. Future work could use this methodology to understand the impact of cognitive load during dynamic activities. Variation in P3 across days, stress level, cognitive function, and levels of motor impairment for a range of dynamic tasks is important to identify in order to understand factors that influence cognitive load.