Eye-Tracking-Based Trail-Making Test to detect cognitive impairment: History
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Jyotsna Chandrasekharan
,
Amudha Joseph
,
Amritanshu Ram
,
Giandomenico Nollo

The growing number of people with cognitive impairment will significantly increase healthcare demand. Screening tools are crucial for detecting cognitive impairment due to a shortage of mental health experts aiming to improve the quality of life for those living with this condition. Eye tracking is a powerful tool that can provide deeper insights into human behavior and inner cognitive processes. The proposed Eye-Tracking-Based Trail-Making Test, ETMT, is a screening tool for monitoring a person’s cognitive function.

  • eye tracking
  • trail-making test
  • adaptive neuro-fuzzy-inference system

1. Introduction

People’s lives have changed significantly in modern times, and some may find it challenging to recall details, understand novel information, remember, pay attention to details, or make judgments that may affect their daily lives [1]. The subtle changes in human beings’ cognitive functions influence a person’s behavior. If a person has trouble remembering, learning new things, and concentrating on their work, their cognitive ability is deteriorating or affected by cognitive impairment [2]. Cognitive impairment is regarded as one of the most costly diseases, considering the cost of drugs and nursing facilities [3]. Cognitive impairment is considered an incurable disease [4][5]. However, the growth of the disease can be decreased by providing adequate treatment and care if it can be diagnosed in an earlier stage.
Nowadays, there is a considerable increase in people suffering from this disability [6], and the rapid increase in people with dementia has become a significant health issue. However, the progression of cognitive impairment can be slowed down with early detection and prompt treatments [7]. Though it is mainly observed after the age of 65, it is not limited to any specific age group. Other risk factors for cognitive impairment include family history, injury to the brain, exposure to toxicants, brain irradiation [8], education level, and other diseases. The side effects of some medications, deficiency of vitamins, depression, and other health issues can also be the reason for mild cognitive impairment (MCI) [9].
Cognitive impairment ranges from mild to intense. The transitional stage from subtle cognitive abnormalities to the early stages of dementia is known as MCI [10][11]. People with mild disabilities have minute changes in their cognitive function but can manage their daily activities. Intense levels of impairment can result in a loss of the ability to speak, write, and understand the meaning or significance, resulting in the inability to live independently. Cognitive impairment can influence a person’s mental flexibility, concentration, visual attention, and focused attention.
Trained professionals mainly administer commonly available neuropsychological tests, such as Mini-Mental State Examination (MMSE) [12], Montreal Cognitive Assessment (MoCA) [13], and Trail-Making Test (TMT) [14], to detect dementia. The traditional tests usually follow the pen-and-paper method. Patients experience psychological stress as a result of having to respond to a succession of queries for a longer period of time. If the patient has a problem with writing, this can affect the score generated with traditional screening methods. Eye-tracking technology has gained a significant role in screening cognitive and neurological problems [15]. Eye-tracking technology, which can monitor eye movements in a less intrusive way, helps to tackle this situation by assessing cognitive decline [16].
It is essential to identify people with signs of cognitive impairment and ensure their care and treatment by healthcare professionals. Considering the significant role of eye-tracking technology in determining cognitive problems, researchers propose a screening tool for the Eye-Tracking-Based Trail-Making Test (ETMT) to support healthcare professionals [8].

2. Conventional Methods

There are numerous ways to identify a person’s cognitive impairment. Various neuropsychological tests are conducted with the assistance of trained healthcare professionals. MMSE [12], TMT [14], Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) [7], and Frontal Assessment Battery (FAB) [7] are some of the frequently used tests to diagnose cognitive impairment and should be carried out by professional evaluators. While these neuropsychological tests are valid and trustworthy, they are not simple or brief enough to be utilized as regular dementia screening tools. MMSE [17] and MoCA [13] are some of the clinical tools to assess cognitive impairment. But they have limitations in identifying mild cognitive impairment. These scores can also be affected by age and education level.
Traditional methods use self-reporting questionnaires to detect cognitive decline in a person. There are well-validated and standardized questionnaires to detect cognitive decline, but they can be psychologically biased. Memory lapse, carelessness, or if the respondent voluntarily changes the feedback can affect the detection of cognitive changes.
Despite being accurate and reliable, these conventional cognitive tests have certain limitations. Senior patients may take a longer time to complete these tests. A more precise evaluation is required to detect the severity of cognitive impairment. Since they must respond to several questions during the examination, subjects may feel psychological stress. Since the proficiency of the assessor can influence the outcomes, these tests must be correctly administered by professional neuropsychologists with extensive training. Since some of these cognitive tests include writing and drawing, patients with motor dysfunction, which is frequently present in people with dementia, can impact the results.

3. Eye-Tracking Techniques

Eye-tracking data have a significant role in capturing the involuntary physiological responses of a person, which accurately helps to understand the characteristics or cognitive inhibitions of an individual [18]. Clinical assessments may miss identifying cognitive inhibitions in the earlier stage of disease, whereas simple eye-tracking measures can point that out [5][19].
Eye-tracking measures can distinguish among subtypes of mild cognitive impairment [19]. The antisaccadic task’s error rate was considered a significant measure to classify the types of mild cognitive impairment. Digit Span [20] and Spatial [21] Tests were performed on participants in the 55–90 age group. The analysis of variance (ANOVA) test could significantly point to the difference in the error rates in the antisaccadic tasks performed by different types of mildly cognitively impaired participants.
In a study performed with several memory tasks, the eye-tracking based score correlated with the MMSE score and could discriminate the control group from the Alzheimer’s disease (AD) and MCI groups [12]. Gaze tracking is widely accepted as a tool for monitoring a person’s cognitive functions and neurological disorders [22].
Eye-tracking technology can be utilized to assess and monitor various aspects of diseases like amyotrophic lateral sclerosis (ALS), AD, Parkinson’s disease (PD) [23], multiple sclerosis (MS), and epilepsy [24]. Eye tracking can detect early cognitive impairment by observing how individuals perform tasks such as looking away from sudden stimuli (antisaccadic task), smoothly tracking moving targets (smooth-pursuit task), and efficiently scanning visual scenes (visual-scanning task). Difficulty or errors in these tasks can indicate early changes in inhibitory control, motor control, attention, and scanning abilities, providing valuable insights into cognitive decline before conventional evaluations can detect them [19].

4. Cognitive Impairment Associated with Diseases

ALS patients exhibit cognitive and behavioral deficits due to their motor impairment. A higher antisaccadic error rate and saccadic latency were observed in these patients. In the successive stages of this disease, the patient loses the ability to write and speak, making the conventional paper–pencil method an inappropriate cognitive assessment tool [24]. The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is a standardized method to assess ALS patients [25]. The ECAS was developed exclusively for ALS patients and may not be appropriate for measuring cognitive and behavioral changes in people with other neurological disorders. The ECAS does not assess all aspects of cognitive function and is not sensitive to changes in cognitive function in people with early-stage ALS [26].
Lower motor neuron atrophy causes patients to lose their capacity to talk or write; at this point, these conventional measures are no longer appropriate for cognitive assessments [27]. An eye-tracking variant of the ECAS test could reduce the test duration and improve the evaluation effectiveness. Several studies show that eye tracking is a timely, efficient, and accurate way to evaluate cognitive performance in ALS patients [28][29].
Progressive memory loss is observed in AD patients and leads to dementia [30]. The saccades, fixations, and smooth pursuit performed by a patient could yield more accurate inferences of the stage of the disease.AD patients are slower in fixating on a target and have reduced fixation spans and less precise saccadic motions [24]. Gradual loss of attention and deterioration in visual attention were observed in AD patients [31].
While performing memory tasks, impaired visual attention and diminished visual interest were observed in AD patients. The memory-and-recall task [32][33], deductive reasoning [12], the working memory task [7], etc., helped to discriminate AD from the control group. Visual working memory tasks can also differentiate AD from MCI. A deductive reasoning task can distinguish the MCI group from the normal control group. The Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) and the MMSE [34] are some of the established standard tests used to detect Alzheimer’s disease [35]. But these tests take a longer time to complete, and subjects may feel highly stressed due to the pressure of responding to a series of questions.
PD is a neurodegenerative disease that results in the patient’s cognitive decline [36]. In addition, ocular abnormalities and a longer response time were observed while performing saccadic tasks [24]. Degenerative changes can cause the impairment of focused attention. A decline in focused attention was observed in PD patients [37]. The visual search behavior of a PD patient is impaired, and there can be changes in fixational and non-fixational eye movements [38].
Standard conventional assessments like Parkinson’s Disease-Cognition (SCOPA-COG) and the Parkinson’s Disease-Cognitive Rating Scale (PD-CRS) [39] are not very sensitive in detecting cognitive abnormalities in the initial phases of PD. Eye-tracking measures are correlated with Parkinson’s disease severity, indicating that they may be used to forecast disease development in patients.
Disengagement of visual attention was noticed in people with cognitive impairment in their older age [31]. ’Gap’ and ’overlap’ conditions were introduced while displaying the stimulus to understand the saccadic movements of the participants. A blank screen was introduced between the fixation and target displays under the first condition, whereas an overlap of fixation and target was introduced between the fixation and target displays. Prosaccadic errors were observed in participants with dementia and old age.
The memory test visual paired-comparison (VPC) task is used to understand memory impairment [40]. Eye movement features like fixations, saccades, and re-fixations were considered to understand the participants’ behavior while viewing novel and repeated stimuli. The machine learning algorithm support vector machine (SVM) could accurately distinguish the normal control group from the MCI group. The VPC task with eye-tracking technology was used as a screening tool to detect MCI [41]. Eye-tracking features like total looking time, fixation count, and percentage of time spent on the novel image were considered for analysis. The novel image was viewed by the control group and PD patients for more than 70% of their total viewing time but only for 53% of the time by the MCI group.
A deficit in sustained attention is observed in the early stage of dementia and leads to severe impairment in the later stage [42]. An earlier diagnosis helps to slow down the disease’s growth in a person with MCI [43][44][45]. But, unfortunately, the clinical biomarkers to detect these diseases earlier are quite expensive and invasive [19]. Eye tracking provides non-invasive and involuntary measures that serve as a critical biomarker in the earlier diagnosis of these diseases. Advanced neurophysiological eye-tracking measures are gaining researchers’ attention, considering the low responsiveness of conventional approaches to identify cognitive abnormalities in the initial phases of disorders [24].
It is challenging to detect MCI in an early stage based on clinical evaluations [46]. However, the eye-tracking measures [47] captured while performing the working memory task can distinguish healthy individuals from people with cognitive impairment. When conventional assessment methods fail to detect minute impairment, simple eye-tracking techniques can pinpoint that impairment. Various tasks can be used to understand the different types of cognitive-impairment diseases. Different types of tests are performed to understand different cognitive-impairment diseases [43].
The myelin sheath, optic neurons, and the spinal cord are all harmed by the disease MS [24]. The impairment of memory, executive function, and attention is increasingly recognized as a substantial functional weakness in MS. Neurophysiological examinations and brain MRI have traditionally been adopted as diagnostic tools. Using eye tracking, abnormal visuospatial behavior in MS can be detected. The most popular method for evaluating oculomotor function in MS patients is the use of saccadic tasks because of their direct relationship with ocular-nerve damage.
Cognitive impairment associated with stroke includes deficits in attention, memory, language, and executive functions [48]. These impairment types significantly impact the quality of life in stroke survivors, and there is a high potential for the development of dementia within the first year of stroke onset. After a stroke, cognitive impairment, including memory problems, can be detected with neuropsychological assessments such as MMSE, MoCA, and TMT, and language tests like phonological and semantic fluency token tests. An eye-tracking-based study was performed in stroke patients with cognitive impairment [49]. They underwent eye-tracking linkage attention training and showed significant improvements in visuospatial and memory tests, suggesting that eye-tracking measures can detect cognitive impairment and contribute to its rehabilitation in stroke patients.
People with Huntington’s disease (HD) can experience cognitive decline that makes it difficult for them to think, remember, and learn. This can happen even before the motor symptoms of HD appear [50]. Researchers found that premanifest (Pre-HD) people made more errors and took longer to respond when the task required inhibitory control, working memory, or fronto-executive function [51]. People with HD have lower saccadic latency and are more likely to make disinhibited saccades [52]. Eye tracking is a promising tool for detecting changes in saccadic eye movements, which could serve as biomarkers for tracking Huntington’s disease (HD) progression.
The various diseases associated with cognitive impairment, the standard assessment tools used to detect impairment and these tools’ shortcomings, and the eye-tracking measures used for impairment detection are shown in Table 1. The various tasks performed to screen mild cognitive impairment are memory tasks [7], attention and calculation tasks [7], TMT-A, TMT-B [14], the Word Memory Test [53], identifying some objects, and counting backward [54]. People with AD progressively lose their ability to exert effective inhibitory control over their actions and their attention. In particular, the capacity to control and inhibit voluntary gaze shifting away and towards the salient stimulus is impaired. Physicians have a significant opportunity with eye tracking to diagnose Alzheimer’s disease [55] in its earliest stages during the MCI phase [56].
Table 1. Cognitive impairment and associated factor analysis.
Paper Disease Cognitive
Impairment		 Stimulus Standard
Cognitive
Assessment
Tools		 Shortcomings of
Standard
Assessment Tools		 Eye-Tracking
Measures		 Observations
[24] ALS Motor impairment Visual paired-
comparison task		 ECAS Cannot handle lower
motor neuron atrophy		 Antisaccadic
error rate,
saccadic latency		 Higher antisaccades,
error rate,
saccadic latency
[7]
[12]
[24]
[31]
[32]
[33]		 AD Memory impairment,
impaired visual attention,
attentional disengagement		 Working memory tasks,
deductive reasoning,
memory recall task,
visual memory task		 ADAS-Cog,
MMSE,
MOCA		 Longer time duration,
not simple,
subjects may feel
highly stressed		 Saccades,
fixations,
smooth pursuit		 Longer time to
fixate the target,
shorter fixation
duration,
imprecise saccadic
movements
[24]
[57]		 PD Focused attention
impairment,
movement impairment,
memory impairment		 Saccadic task, TMT SCOPA-COG
PD-CRS,
MOCA		 Low sensitivity
in recognizing cognitive
deficits in the early
stages of PD		 Pupil diameter Ocular abnormalities,
longer response time
[40]
[41]
[43]		 MCI Memory impairment Visual paired-
comparison task,
animal fluency,
WLM,
constructional praxis,
TMT,
Digit Span Subtest,
Clock Drawing Test		 MMSE,
MOCA		 Expensive, invasive,
cannot detect early
stages of the disease		 Fixations, saccades,
re-fixations,
pupil diameter,
total looking time,
fixation count,
percentage looking
time on novel image		 Percentage time
in viewing
the novel pictures
could differentiate
control group
from MCI group
[24] MS Impairment of attention,
executive function
impairment,
memory impairment		 Saccadic task,
ocular working
memory task		 MRI,
MACFIMS		 A trained evaluator
needs at least 90 min
for a full evaluation		 Fixations,
saccadic latency		 Fixation instability,
higher saccadic
error rates,
impaired pursuit
[24] Epilepsy Neuropsychological
impairment		 Vision-guided saccades,
antisaccadic response
inhibition,
prosaccades,
antisaccadic tasks		 ET,
PNS		 Limited sensitivity,
unsuitability for
repeated assessment,
sole focus on one aspect
of cognition		 Saccades, fixations Increased error rate,
longer reflexive time
at the start of saccades

5. Trail-Making Test

The TMT is a neuropsychological test [58] that measures the capability of the brain to perform visual attention and task switching [59]. It provides details regarding executive functioning, visual search speed, scanning, processing speed, focused attention, and mental flexibility. Many types of brain dysfunction, especially those affecting the frontal lobes, can be detected and diagnosed using the TMT. This area of the brain regulates high-level cognitive skills, such as planning, consciousness, memory, emotion, and attention. It was used as a standard test to evaluate soldiers’ brain damage during the Second World War. The test was administered using paper and a pencil in a conventional manner. To identify cognitive impairment, only the error rate and overall completion time were taken into consideration and could not offer a detailed analysis [60]. The TMT’s objective is to carry out the examinations as promptly and precisely as possible in order to identify any potential indicators of cognitive impairment. A psychologist should assist the patient throughout the conduct of the test. People who suffer from motor impairment may have difficulty in completing the test and take longer to complete it. There have been studies on different TMT variants to overcome the limitations of existing conventional methods.
The digital TMT (dTMT) precisely monitors a variety of distinct elements along with the overall completion time and the number of errors, such as the number of viewing pauses, the duration of each pause, lifting, lifting duration, duration within the circle, and the amount of time across the circles [61].
The benefits of combining infrared eye tracking with the TMT task is another significant factor [62]. Research on infrared eye tracking is becoming well known for its ability to diagnose cognitive impairment. Eye tracking was employed in an ongoing study to gather objective, quantitative data on each participant’s visual, attentional, and memory functions [63]. In addition to the basic task-completion-time metric, eye tracking enabled a number of potentially insightful and sensitive measurements.

6. Summary

The study of related works revealed a variety of cognitive deficits, including memory loss, lower visual interest, atypical visuospatial behavior, attentional disengagement, motor impairment, and impaired mobility, and their association with diseases like AD, PD, MCI, MS, ALS, HD, stroke, and epilepsy. Eye-tracking features like inattentional blindness, error rate, total completion time, scanpath comparison score, fixation duration, saccadic latency, and smooth pursuit could allow for the drawing of inferences to understand those deficits, as shown in Table 2. The eye-tracking version of the TMT provides those eye-tracking features and can allow for the drawing of inferences on detecting cognitive impairment.
Table 2. Eye-tracking features for the detection of cognitive impairment in different diseases.
Feature Impairment Disease
Inattentional blindness Memory PD, AD, MCI
Error rate Memory, imprecise saccadic movement AD, ALS, MCI, MS
Total completion time Memory AD, PD, MCI
Scanpath comparison score Visual attention, diminished visual curiosity,
abnormal visuospatial behavior		 AD
Shorter fixation duration Attentional disengagement AD, MS
Higher saccadic latency Motor impairment ALS
Fixation instability Impaired mobility and cognition MS
Impaired pursuit Impaired mobility and cognition MS
In a previous study, [1], a comparison of various versions of the TMT that are performed digitally, using paper–pencil, or with eye tracking was reported. The benefits of the eye-tracking version of the TMT were also pointed out based on the findings. The study presented individual and group profiles, which aids in comprehending a group of individuals performing the TMT with a similar level of cognitive decline [1].
The proposed ETMT model is a screening tool for cognitive impairment based on the changes observed in eye-tracking measures while performing eye-tracking versions of TMT-A and TMT-B. The ETMT model provides a detailed understanding of the participant’s focused attention and visual search speed other than their cognitive impairment. By utilizing eye-tracking technology, the model enhances practitioners’ understanding of these cognitive aspects, enabling a more detailed evaluation of participants’ cognitive abilities and potential deficits.

This entry is adapted from the peer-reviewed paper 10.3390/s23156848

References

  1. Jyotsna, C.; Amudha, J.; Rao, R.; Nayar, R. Intelligent gaze tracking approach for trail making test. J. Intell. Fuzzy Syst. 2020, 38, 6299–6310.
  2. Wasef, S.; Laksono, I.; Kapoor, P.; Tang-Wei, D.; Gold, D.; Saripella, A.; Riazi, S.; Islam, S.; Englesakis, M.; Wong, J.; et al. Screening for subjective cognitive decline in the elderly via subjective cognitive complaints and informant-reported questionnaires: A systematic review. BMC Anesthesiol. 2021, 21, 277.
  3. Conner, K.; Sweeney, C.Y.; Brown, T.; Childs, L.; Rogers, S.; Gregory, T. Practical applications of physical activity for successful cognitive aging. JAAPA 2017, 30, 30–35.
  4. Whitson, H.E.; Malhotra, R.; Chan, A.; Matchar, D.B.; ∅stbye, T. Comorbid visual and cognitive impairment: Relationship with disability status and self-rated health among older Singaporeans. Asia Pac. J. Public Health 2014, 26, 310–319.
  5. Pu, L.; Pan, D.; Wang, H.; He, X.; Zhang, X.; Yu, Z.; Hu, N.; Du, Y.; He, S.; Liu, X.; et al. A predictive model for the risk of cognitive impairment in community middle-aged and older adults. Asian J. Psychiatry 2023, 79, 103380.
  6. Morimoto, R.; Kawanaka, H.; Hicks, Y.; Setchi, R. Development of Recreation Game for Measurement of Eye Movement Using Tangram. Procedia Comput. Sci. 2021, 192, 4924–4932.
  7. Oyama, A.; Takeda, S.; Ito, Y.; Nakajima, T.; Takami, Y.; Takeya, Y.; Yamamoto, K.; Sugimoto, K.; Shimizu, H.; Shimamura, M.; et al. Novel method for rapid assessment of cognitive impairment using high-performance eye-tracking technology. Sci. Rep. 2019, 9, 12932.
  8. Surendran, H.P.; Narmadha, M.; Kalavagunta, S.; Sasidharan, A.; Dutta, D. Preservation of cognitive function after brain irradiation. J. Oncol. Pharm. Pract. 2022, 28, 1182–1188.
  9. Jorde, R.; Mathiesen, E.B.; Rogne, S.; Wilsgaard, T.; Kjærgaard, M.; Grimnes, G.; Schirmer, H. Vitamin D and cognitive function: The Tromsø Study. J. Neurol. Sci. 2015, 355, 155–161.
  10. Petersen, R.C.; Negash, S. Mild cognitive impairment: An overview. CNS Spectrums 2008, 13, 45–53.
  11. Carvalho, C.M.; Seixas, F.L.; Conci, A.; Muchaluat-Saade, D.C.; Laks, J.; Boechat, Y. A dynamic decision model for diagnosis of dementia, Alzheimer’s disease and Mild Cognitive Impairment. Comput. Biol. Med. 2020, 126, 104010.
  12. Tadokoro, K.; Yamashita, T.; Fukui, Y.; Nomura, E.; Ohta, Y.; Ueno, S.; Nishina, S.; Tsunoda, K.; Wakutani, Y.; Takao, Y.; et al. Early detection of cognitive decline in mild cognitive impairment and Alzheimer’s disease with a novel eye tracking test. J. Neurol. Sci. 2021, 427, 117529.
  13. Nasreddine, Z.S.; Phillips, N.A.; Bédirian, V.; Charbonneau, S.; Whitehead, V.; Collin, I.; Cummings, J.L.; Chertkow, H. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 2005, 53, 695–699.
  14. Guo, Y. A selective review of the ability for variants of the Trail Making Test to assess cognitive impairment. In Applied Neuropsychology: Adult; Taylor & Francis: Abingdon, UK, 2021; pp. 1–12.
  15. Haque, R.U.; Pongos, A.L.; Manzanares, C.M.; Lah, J.J.; Levey, A.I.; Clifford, G.D. Deep convolutional neural networks and transfer learning for measuring cognitive impairment using eye-tracking in a distributed tablet-based environment. IEEE Trans. Biomed. Eng. 2020, 68, 11–18.
  16. Belkhiria, C.; Peysakhovich, V. EOG metrics for cognitive workload detection. Procedia Comput. Sci. 2021, 192, 1875–1884.
  17. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12, 189–198.
  18. Jyotsna, C.; Amudha, J. Eye gaze as an indicator for stress level analysis in students. In Proceedings of the 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, 19–22 September 2018; pp. 1588–1593.
  19. Wilcockson, T.D.; Mardanbegi, D.; Xia, B.; Taylor, S.; Sawyer, P.; Gellersen, H.W.; Leroi, I.; Killick, R.; Crawford, T.J. Abnormalities of saccadic eye movements in dementia due to Alzheimer’s disease and mild cognitive impairment. Aging 2019, 11, 5389.
  20. Woods, D.L.; Kishiyama, M.M.; Yund, E.W.; Herron, T.J.; Edwards, B.; Poliva, O.; Hink, R.F.; Reed, B. Improving digit span assessment of short-term verbal memory. J. Clin. Exp. Neuropsychol. 2011, 33, 101–111.
  21. Wiechmann, A.; Hall, J.R.; O’Bryant, S.E. The utility of the spatial span in a clinical geriatric population. Aging Neuropsychol. Cogn. 2010, 18, 56–63.
  22. Martinez-Marquez, D.; Pingali, S.; Panuwatwanich, K.; Stewart, R.A.; Mohamed, S. Application of eye tracking technology in aviation, maritime, and construction industries: A systematic review. Sensors 2021, 21, 4289.
  23. Akshay, S.; Vincent, K. Identification of Parkinson disease patients classification using feed forward technique based on speech signals. Int. J. Eng. Adv. Technol 2019, 8, 1769–1778.
  24. Tao, L.; Wang, Q.; Liu, D.; Wang, J.; Zhu, Z.; Feng, L. Eye tracking metrics to screen and assess cognitive impairment in patients with neurological disorders. Neurol. Sci. 2020, 41, 1697–1704.
  25. Saxon, J.A.; Thompson, J.C.; Harris, J.M.; Ealing, J.; Hamdalla, H.; Chaouch, A.; Young, C.; Blackburn, D.; Majeed, T.; Gall, C.; et al. The Edinburgh Cognitive and Behavioral ALS Screen (ECAS) in frontotemporal dementia. Amyotroph. Lateral Scler. Front. Degener. 2020, 21, 606–613.
  26. Taule, T.; Morland, A.S.; Renså, M.A.; Aßmus, J.; Tysnes, O.B.; Rekand, T. Edinburgh Cognitive and Behavioral Amyotrophic Lateral Sclerosis Screen (ECAS) in Norway: Protocol for validation and a prospective cohort study. Contemp. Clin. Trials Commun. 2019, 14, 100347.
  27. Keller, J.; Gorges, M.; Horn, H.T.; Aho-Özhan, H.E.; Pinkhardt, E.H.; Uttner, I.; Kassubek, J.; Ludolph, A.C.; Lulé, D. Eye-tracking controlled cognitive function tests in patients with amyotrophic lateral sclerosis: A controlled proof-of-principle study. J. Neurol. 2015, 262, 1918–1926.
  28. Keller, J.; Krimly, A.; Bauer, L.; Schulenburg, S.; Böhm, S.; Aho-Özhan, H.E.; Uttner, I.; Gorges, M.; Kassubek, J.; Pinkhardt, E.H.; et al. A first approach to a neuropsychological screening tool using eye-tracking for bedside cognitive testing based on the Edinburgh Cognitive and Behavioural ALS Screen. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 443–450.
  29. Proudfoot, M.; Menke, R.A.; Sharma, R.; Berna, C.M.; Hicks, S.L.; Kennard, C.; Talbot, K.; Turner, M.R. Eye-tracking in amyotrophic lateral sclerosis: A longitudinal study of saccadic and cognitive tasks. Amyotroph. Lateral Scler. Front. Degener. 2016, 17, 101–111.
  30. Abazid, M.; Houmani, N.; Dorizzi, B.; Boudy, J.; Mariani, J.; Kinugawa, K. Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline. Bioengineering 2022, 9, 62.
  31. Polden, M.; Wilcockson, T.D.; Crawford, T.J. The disengagement of visual attention: An eye-tracking study of cognitive impairment, ethnicity and age. Brain Sci. 2020, 10, 461.
  32. Li, X.; Jia, S.; Zhou, Z.; Jin, Y.; Zhang, X.; Hou, C.; Zheng, W.; Rong, P.; Jiao, J. The role of the Montreal Cognitive Assessment (MoCA) and its memory tasks for detecting mild cognitive impairment. Neurol. Sci. 2018, 39, 1029–1034.
  33. Fraga, F.J.; Mamani, G.Q.; Johns, E.; Tavares, G.; Falk, T.H.; Phillips, N.A. Early diagnosis of mild cognitive impairment and Alzheimer’s with event-related potentials and event-related desynchronization in N-back working memory tasks. Comput. Methods Programs Biomed. 2018, 164, 1–13.
  34. Lestari, S.; Mistivani, I.; Rumende, C.M.; Kusumaningsih, W. Comparison between mini mental state examination (MMSE) and Montreal cognitive assessment Indonesian version (MoCA-Ina) as an early detection of cognitive impairments in post-stroke patients. J. Physics Conf. Ser. 2017, 884, 012153.
  35. Araújo, T.; Teixeira, J.P.; Rodrigues, P.M. Smart-data-driven system for alzheimer disease detection through electroencephalographic signals. Bioengineering 2022, 9, 141.
  36. Russo, M.; Amboni, M.; Barone, P.; Pellecchia, M.T.; Romano, M.; Ricciardi, C.; Amato, F. Identification of a Gait Pattern for Detecting Mild Cognitive Impairment in Parkinson’s Disease. Sensors 2023, 23, 1985.
  37. Sharpe, M. Auditory attention in early Parkinson’s disease: An impairment in focused attention. Neuropsychologia 1992, 30, 101–106.
  38. Beylergil, S.B.; Kilbane, C.; Shaikh, A.G.; Ghasia, F.F. Eye movements in Parkinson’s disease during visual search. J. Neurol. Sci. 2022, 440, 120299.
  39. Kulisevsky, J.; Pagonabarraga, J. Cognitive impairment in Parkinson’s disease: Tools for diagnosis and assessment. Mov. Disord. 2009, 24, 1103–1110.
  40. Lagun, D.; Manzanares, C.; Zola, S.M.; Buffalo, E.A.; Agichtein, E. Detecting cognitive impairment by eye movement analysis using automatic classification algorithms. J. Neurosci. Methods 2011, 201, 196–203.
  41. Crutcher, M.D.; Calhoun-Haney, R.; Manzanares, C.M.; Lah, J.J.; Levey, A.I.; Zola, S.M. Eye tracking during a visual paired comparison task as a predictor of early dementia. Am. J. Alzheimer’s Dis. Other Dementias 2009, 24, 258–266.
  42. Huntley, J.D.; Hampshire, A.; Bor, D.; Owen, A.M.; Howard, R.J. The importance of sustained attention in early Alzheimer’s disease. Int. J. Geriatr. Psychiatry 2017, 32, 860–867.
  43. Liu, Z.; Yang, Z.; Gu, Y.; Liu, H.; Wang, P. The effectiveness of eye tracking in the diagnosis of cognitive disorders: A systematic review and meta-analysis. PLoS ONE 2021, 16, e0254059.
  44. Karim, M.; Venkatachalam, J. A randomized pilot study of a cognitive intervention for the elderly with Mild Cognitive Impairment. ECS Trans. 2022, 107, 8383.
  45. Mannan, F.A.; Porffy, L.A.; Joyce, D.W.; Shergill, S.S.; Celiktutan, O. Automatic Detection of Cognitive Impairment with Virtual Reality. Sensors 2023, 23, 1026.
  46. Huggins, C.J.; Escudero, J.; Parra, M.A.; Scally, B.; Anghinah, R.; De Araújo, A.V.L.; Basile, L.F.; Abasolo, D. Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing. J. Neural Eng. 2021, 18, 046087.
  47. Venugopal, D.; Amudha, J.; Jyotsna, C. Developing an application using eye tracker. In Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 20–21 May 2016; pp. 1518–1522.
  48. Al-Qazzaz, N.K.; Ali, S.H.; Ahmad, S.A.; Islam, S.; Mohamad, K. Cognitive impairment and memory dysfunction after a stroke diagnosis: A post-stroke memory assessment. Neuropsychiatr. Dis. Treat. 2014, 10, 1677–1691.
  49. Moon, S.J.; Park, C.H.; Jung, S.I.; Yu, J.W.; Son, E.C.; Lee, H.N.; Jeong, H.; Jang, S.; Park, E.; Jung, T.D. Effects of an eye-tracking linkage attention training system on cognitive function compared to conventional computerized cognitive training system in patients with stroke. Healthcare 2022, 10, 456.
  50. Paulsen, J.S. Cognitive impairment in Huntington disease: Diagnosis and treatment. Curr. Neurol. Neurosci. Rep. 2011, 11, 474–483.
  51. Júlio, F.; Caetano, G.; Januário, C.; Castelo-Branco, M. The effect of impulsivity and inhibitory control deficits in the saccadic behavior of premanifest Huntington’s disease individuals. Orphanet J. Rare Dis. 2019, 14, 246.
  52. Turner, T.H.; Goldstein, J.; Hamilton, J.M.; Jacobson, M.; Pirogovsky, E.; Peavy, G.; Corey-Bloom, J. Behavioral measures of saccade latency and inhibition in manifest and premanifest Huntington’s disease. J. Mot. Behav. 2011, 43, 295–302.
  53. Tomer, E.; Lupu, T.; Golan, L.; Wagner, M.; Braw, Y. Eye tracking as a mean to detect feigned cognitive impairment in the Word Memory Test. In Applied Neuropsychology: Adult; Taylor & Francis: Abingdon, UK, 2018.
  54. Leon-Dominguez, U.; Martín-Rodríguez, J.F.; León-Carrión, J. Executive n-back tasks for the neuropsychological assessment of working memory. Behav. Brain Res. 2015, 292, 167–173.
  55. Vigo, I.; Coelho, L.; Reis, S. Speech-and Language-Based Classification of Alzheimer’s Disease: A Systematic Review. Bioengineering 2022, 9, 27.
  56. Nie, J.; Qiu, Q.; Phillips, M.; Sun, L.; Yan, F.; Lin, X.; Xiao, S.; Li, X. Early diagnosis of mild cognitive impairment based on eye movement parameters in an aging Chinese population. Front. Aging Neurosci. 2020, 12, 221.
  57. Aarsland, D.; Batzu, L.; Halliday, G.M.; Geurtsen, G.J.; Ballard, C.; Ray Chaudhuri, K.; Weintraub, D. Parkinson disease-associated cognitive impairment. Nat. Rev. Dis. Prim. 2021, 7, 47.
  58. Faria, C.d.A.; Alves, H.V.D.; Charchat-Fichman, H. The most frequently used tests for assessing executive functions in aging. Dement. Neuropsychol. 2015, 9, 149–155.
  59. Zucchella, C.; Federico, A.; Martini, A.; Tinazzi, M.; Bartolo, M.; Tamburin, S. Neuropsychological testing. Pract. Neurol. 2018, 18, 227–237.
  60. Lange, R.T.; Iverson, G.L.; Zakrzewski, M.J.; Ethel-King, P.E.; Franzen, M.D. Interpreting the trail making test following traumatic brain injury: Comparison of traditional time scores and derived indices. J. Clin. Exp. Neuropsychol. 2005, 27, 897–906.
  61. Fellows, R.P.; Dahmen, J.; Cook, D.; Schmitter-Edgecombe, M. Multicomponent analysis of a digital Trail Making Test. Clin. Neuropsychol. 2017, 31, 154–167.
  62. Hicks, S.L.; Sharma, R.; Khan, A.N.; Berna, C.M.; Waldecker, A.; Talbot, K.; Kennard, C.; Turner, M.R. An eye-tracking version of the trail-making test. PLoS ONE 2013, 8, e84061.
  63. Ramachandra, C.K.; Joseph, A. IEyeGASE: An intelligent eye gaze-based assessment system for deeper insights into learner performance. Sensors 2021, 21, 6783.
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