Performance Enhancement using BM-BCI in Sports

Abhishek Yadav and Ravinder Agarwal

Abstract: Performance Enhancement using BM-BCI in Sports for retraining brainwave activity 

Neurofeedback has been shown to have the potential for quieting the mind to improve concentration and focus, to improve cognitive function and emotional control. In the present study human brain was seen as an electrochemical machine capable of receiving stimuli and adapt accordingly. 

Only relevant EEG activity was fed back to the trainee by a Brain-Computer Interface (BCI) in an intelligible way, allowing the identification of phasic changes in the EEG and what cognitive state caused it. The results from this study showed that it is possible to learn to change some rhythmical activity in the EEG, after a few feedback sessions and obtain the Performance Enhancement.

1. Introduction

Information  travels through  nerve cells in the nervous system in the form of electrical impulses. When measuring the electric field on the scalp, it is possible to observe  the influence of these electrical impulses that occur in the nerve cells in the neocortex. The resulting signal is known as the electroencephalogram (EEG). 

Training generally consists of placing a couple of electrodes on the scalp and one or two electrodes on the ear lobes. Then the EEG equipment provides real-time, instantaneous audio and visual feedback to the subject about his or her brainwave activity. No electrical current is put into the brain. 

The brain’s electrical activity is simply relayed to the computer. At first, the changes are short-lived, but the changes gradually become more enduring, and with continuing feedback, coaching, and practice, improved brain functioning can usually betrained in most people and the changes are enduring. 

Neurofeedback provides us with opportunities for both enhancing intellectual and sports performance without the use of performance enhancing drugs. There are several areas in which neurofeedback holds particular promise in sports, including enhancement of concentration and attention, reduction of anxiety, improving control over emotions (e.g., anger), and for improving physical balance. 

Different sports place different demands on the brain. Increasingly sophisticated research has begun delineating brainwave pattensassociated with various types of peak performance. In such cases this provides us with important data that may guide the use of neurofeedback training to promote peak performance.

2. Methodology

2.1. Subject Selection

Cognitive abilities of 30 sports person will be done as per ethical norms and with the approval of Institutional ethics committee. Informed consent will be taken from all the participants. Objective evaluation will be done withstress questionnaire, personality questionnaire. Demographic information (age, gender and medical records) and anthropometric measurement, including height, weight and BMI etc. will be kept as a record.

2.2. Subject Selection

  1. Control group (Not a sportsperson): 10 person
  2. Control group (sportsperson without training): 10 
  3. Experimental group (sportsperson with training): 10

2.3. Inclusion Criteria

  1. Subjects with normal physiological condition, 
  2. Players regularly performing in sports events 
  3. Normal, healthy subjects with age 18-40 yrs

2.4. Exclusion Criteria

  1. Associated with any endocrine disorders or circulatory disorders e.g. Diabetes, Hypertension etc. 
  2. A recent physical injuries or surgery.


  1. Nexus-10, the instrument isin use, for acquiring ECG signals and also a biofeedback device.
  2. Laptops, one to give training to the player and other to show feedback signal to the trainer.
  3. Disposable electrodes to take signals.
  4. Sound proof and well ventilated room to avoid external interference and suffocation.


4.1 Practical and Technical Approaches

A substantial problem in EEG is obtaining ‘clean’ data on cerebral activity, i.e. uncontaminated by non-cerebral artifacts. Physiologic artifacts (e.g. muscular activity and eye blinks) are generatedfrom the body, while extraphysiologic artifacts (e.g. environmentalelectrical noise) originates from sources outside the body.Physiologic artifacts tend to be a particular problem when recording EEG from a subject who is in motion. This may account for whystudies of EEG in sports have generally been confined to disciplinesinvolving relatively minimal head movement, such as golf, stationary bike cycling and rifle shooting. Nevertheless, two complementary approaches exist substantially to reduce or eliminate artifacts. The first is to minimize movement artifacts during the recording itself and the  second requires subsequent signal processing of the data via computational methods to remove artifacts.

Artifact removal (computational methods) nevertheless, modern advance in signal processing has occurred with the advent of independent component analysis or ICA. ICA is a higher order statistical method developed to extract individual signals (referred to ascomponents) from mixtures of signals, based on the assumptionthat different physical processes (referred to as sources) will generate unrelated signals.It should be borne in mind that different ICA algorithms (popular ones include Infomax, SOBI, Fast ICA and JADE) are likely to bestdetect specific types of artifacts. The JADE algorithm, for example,may be particularly effective for tackling muscle artifact. Complete artifact elimination may therefore require selection of one or more ICA algorithms.


EEG represents a useful methodological tool in understandingcortical processes that underlie performance in sports and nonsports domains. Although, EEG lacks the spatial resolution ofmore expensive methods such as MEG or fMRI, it offers excellenttemporal resolution and with advances in wireless hardware andequipment portability, allows a freedom of movement almostimpossible to achieve with other neuroimaging technologies.Recording EEG during motion does present a number of problemswith respect to obtaining ‘clean’ cerebral data. However, carefulattention to proper methodological practices and developmentsin hardware and computational processing models offer a promising means of minimising, if perhaps not entirely eradicating, theseproblems.


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