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The Electroencephalography of the Ideal Performance State Karla A. Kubitz, Ph.D., F.A.C.S.M. Dept. of Kinesiology Towson University, Towson, MD.

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Presentation on theme: "The Electroencephalography of the Ideal Performance State Karla A. Kubitz, Ph.D., F.A.C.S.M. Dept. of Kinesiology Towson University, Towson, MD."— Presentation transcript:

1 The Electroencephalography of the Ideal Performance State Karla A. Kubitz, Ph.D., F.A.C.S.M. Dept. of Kinesiology Towson University, Towson, MD

2 Outline The research on the electroencephalography of the ideal performance state has become increasingly sophisticated. Hatfield et al. (1984)’s pioneering study Lawton et al. (1998)’s review of the literature Selected recent studies However, the adoption of statistical pattern recognition techniques could further increase the sophistication of these studies and our understanding of the ideal performance state. Gevins et al. (1998)’s study Preliminary data illustrating the application of statistical pattern recognition techniques

3 Hatfield, Landers, & Ray (1984) N=17 elite shooters Shooting condition (40 shots) EEG activity (8-12 Hz; from -7.5 s) Alpha increased over time (in the left hemisphere) prior to shot Shooting EEG

4 Lawton et al. (1998) Similarities among the studies Performance condition/ sometimes control condition(s) Focused on psychophysiology of preperformance period Measure(s) of performance Differences between the studies Task(s) performed Surrounding conditions Psychophysiologic al measures

5 Lawton et al. (1998; cont.) Findings Increases in EEG power (in the alpha band; in the left hemisphere) during the preperformance period Preperformance EEG influenced by skill level Findings are typically interpreted as related to arousal, attention, and/or self-talk Recommendations Better psychological theories Better experimental designs More sophisticated electrophysiology Multiple psychophysiological measures Relationships between physiology, psychology, and performance

6 Psychological Theories & Experimental Design Kerick et al. (2001); tested verbal-suppression hypothesis Hillman et al. (2000); compared executed and rejected shots

7 Kerick et al. (2001) N=8 skilled marksman Shooting and control (i.e., holding,dry-firing) conditions Hold (maintain stable position); dry-fire (stable position plus shoot/ w/o aiming) Hz event-related alpha power (i.e., at 4 sites; from –9 s) T3 >T4 alpha power and T3

8 Hillman et al. (2000) N = 7 skilled marksmen 40 shots EEG during executed and rejected shots (from –4 s) EEG activity (8-13 Hz and Hz) M = 16 rejected shots EEG alpha and beta power differed between executed and rejected shots Beta power differences Alpha power differences Rejected shots

9 Sophisticated Electrophysiology Haufler et al. (2000); measured multiple sites and multiple ‘narrow’ frequency bands Deeny et al. (2003); examined EEG coherence

10 Haufler et al. (2000) N=36 shooters (15 expert; 21 novice) Shooting condition (Noptel Shooter Training System), ‘right-brain’ task, and ‘left brain’ task EEG activity (8 sites; 6-7 Hz, 9 Hz, Hz, Hz, Hz; from –6 s) Performance on shooting (but not control) tasks differed by skill level Power at all frequencies differed by skill level during shooting (E>N for lower; E

11 Deeny et al. (2003) N=19 (10 expert marksmen; 9 skilled shooters) EEG coherence (13 sites; 8-10 Hz, Hz; and Hz;–4 s) Experts had lower coherence (i.e., between all left hemisphere sites and Fz; between all midline sites and T3; especially for the low alpha and low beta frequencies) than novices Experts

12 Multiple Psychophysiological Measures Janelle et al. (2000); recorded EEG and eye movement

13 Janelle et al. (2000) 25 rifle shooters (12 expert; 13 nonexpert) Shooting condition (40 shots; Noptel Shooter Training System) EEG activity (11 sites; 8-13 and Hz; from –6 s) and eye movement (5000 SU eye movement system) Performance and eye movement differed by skill level Alpha and beta power differed by skill level Alpha power Beta power Experts

14 Relationships Between Physiological States, Psychological States, and Performance Loze, Collins, & Holmes (2001) Konttinen, Lyytinen, & Viitasalo (1998); measured EEG and rifle stability

15 Loze, Collins, & Holmes (2001) N=6 shooters 60 shot match; simulated competition EEG alpha activity (T3, T4, Oz; from –6 s) from 5 best and 5 worst shots EEG alpha power (at T3 and T4) was greater in left than in right hemisphere EEG alpha power increased across time (at Oz) prior to best and did the opposite prior to worst shots Alpha power in best shots

16 Konttinen, Lyytinen, & Viitasalo (1998) N=12 rifle shooters (6 elite; 6 pre-elite) Shooting task (200 shots; Noptel Shooter Training System) EEG slow potentials (frontal and central sites; from –6 s) and rifle barrel stability Performance, rifle barrel stability, frontal positivity, and central laterality differed by skill level Frontal positivity and central laterality related to rifle stability levels Good stability shots

17 Outline The research on the electroencephalography of the ideal performance state has become increasingly sophisticated. Hatfield et al. (1984; 1987)’s pioneering studies Lawton et al. (1998)’s review Selected recent studies However, the adoption of statistical pattern recognition techniques could further increase the sophistication of these studies and our understanding of the ideal performance state. Gevins et al. (1998)’s study Exploring Haufler et al. (2000)’s data

18 Gevins et al. (1998) N=8 participants; working memory task (3 levels of difficulty) EEG activity (27 sites, 3 bands), reaction times, and accuracy Statistical Pattern Recognition Techniques EEG data segments (4.5 s windows) divided into testing and training subsets Power estimates for 4-7 Hz, 8-12 Hz, and Hz Selected candidate features to submit to classification algorithm

19 Gevins et al. (1998; cont.) Submitted candidate features to classifier Trained classifier to identify ‘best’ multivariate combination of features (i.e., the combination that best discriminates task difficulty) Tested resulting classifier on ability to classify ‘new’ EEG segments Theta and alpha features (at O and P sites) most heavily weighted in classification algorithms Trained on data from one day Tested on new data from same day (98% accuracy) Tested on data from another day (92% accuracy) Trained on data from spatial task/ tested on data from verbal task (94% accuracy) Trained on data from one group / tested on data from a new group (83% accuracy)

20 Application of Statistical Pattern Recognition Techniques to Sport Performance Data Haufler et al. (2000) N=2 (1 expert; 1 nonexpert) shooters EEG data epoched around shot (-1500 to 0) Epochs divided into training and testing sets Classifier trained to distinguish between expert and novice shooter Classifier tested on ability to classify ‘new’ EEG samples 73% accuracy Expert Nonexpert

21 Outline The research on the electroencephalography of the ideal performance state has become increasingly sophisticated. Hatfield et al. (1984; 1987)’s pioneering studies Lawton et al. (1998)’s review Selected recent studies However, the adoption of statistical pattern recognition techniques could further increase the sophistication of these studies and our understanding of the ideal performance state. Gevins et al. (1998)’s study Exploring Haufler et al. (2000)’s data

22 The Electroencephalography of the Ideal Performance State


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